Publications by Tag

The following tags appear in the publications listed in the review:

adversarial API autocomplete benchmark benchmarking bimodal Binary Code clone code completion code generation code similarity compilation completion cybersecurity dataset decompilation defect deobfuscation documentation dynamic edit editing education evaluation execution feature location fuzzing generalizability generation GNN grammar human evaluation information extraction instruction tuning interpretability language model large language models LLM logging memorization metrics migration naming natural language generation natural language processing notebook optimization pattern mining pretraining program analysis program synthesis question answering refactoring repair representation retrieval Reverse Engineering review search static static analysis style summarization survey synthesis test generation tool topic modeling topic modelling traceability Transformer Transformers translation types variable misuse verification vulnerability

Tags

See below a list of all tags and the related papers

🏷 adversarial

• Adversarial Examples for Models of Code Noam Yefet, Uri Alon, Eran Yahav
• Generating Adversarial Examples for Holding Robustness of Source Code Processing Models Huangzhao Zhang, Zhuo Li, Ge Li, Lei Ma, Yang Liu, Zhi Jin
• Embedding Java Classes with code2vec: Improvements from Variable Obfuscation Rhys Compton, Eibe Frank, Panos Patros, Abigail Koay
• Adversarial Robustness for Code Pavol Bielik, Martin Vechev
• On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
• You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion Roei Schuster, Congzheng Song, Eran Tromer, Vitaly Shmatikov
• Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
• Semantic Robustness of Models of Source Code Jordan Henkel, Goutham Ramakrishnan, Zi Wang, Aws Albarghouthi, Somesh Jha, Thomas Reps
• Backdoors in Neural Models of Source Code Goutham Ramakrishnan, Aws Albarghouthi

🏷 API

• Lexical Statistical Machine Translation for Language Migration Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen
• Statistical Learning Approach for Mining API Usage Mappings for Code Migration Anh Tuan Nguyen, Hoan Anh Nguyen, Tung Thanh Nguyen, Tien N. Nguyen
• Learning API Usages from Bytecode: A Statistical Approach Tam The Nguyen, Hung Viet Pham, Phong Minh Vu, Tung Thanh Nguyen
• Parameter-Free Probabilistic API Mining across GitHub Jaroslav Fowkes, Charles Sutton
• Deep API Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim.
• Mapping API Elements for Code Migration with Vector Representations Trong Duc Nguyen, Anh Tuan Nguyen, Tien N. Nguyen
• Function Assistant: A Tool for NL Querying of APIs Kyle Richardson, Jonas Kuhn
• DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim
• Learning Technical Correspondences in Technical Documentation Kyle Richardson, Jonas Kuhn
• Exploring API Embedding for API Usages and Applications Trong Duc Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Tien N. Nguyen
• Finding Likely Errors with Bayesian Specifications Vijayaraghavan Murali, Swarat Chaudhuri, Chris Jermaine
• Polyglot Semantic Parsing in APIs Kyle Richardson, Jonathan Berant, Jonas Kuhn
• Bayesian Sketch Learning for Program Synthesis Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
• Unsupervised Learning of API Aliasing Specifications Jan Eberhardt, Samuel Steffen, Veselin Raychev, Martin Vechev
• SAR: Learning Cross-Language API Mappings with Little Knowledge N. D. Q. Bui, Y. Yu, L. Jiang
• Mining Likely Analogical APIs across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding Chunyang Chen, Zhenchang Xing, Yang Liu, Kent Ong Long Xiong
• AutoPandas: neural-backed generators for program synthesis Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica

🏷 autocomplete

• Learning from Examples to Improve Code Completion Systems Marcel Bruch, Martin Monperrus, Mira Mezini.
• On the Naturalness of Software Abram Hindle, Earl T. Barr, Mark Gabel, Zhendong Su, Premkumar Devanbu
• Code Completion with Statistical Language Models Veselin Raychev, Martin Vechev, Eran Yahav
• Intelligent Code Completion with Bayesian Networks Sebastian Proksch, Johannes Lerch, Mira Mezini
• Graph-based Statistical Language Model for Code Anh Tuan Nguyen, Tien N. Nguyen
• Learning Python Code Suggestion with a Sparse Pointer Network Avishkar Bhoopchand, Tim Rocktaschel, Earl Barr, Sebastian Riedel
• Neural Code Completion Chang Liu, Xin Wang, Richard Shin, Joseph E. Gonzalez, Dawn Song
• Code Completion with Neural Attention and Pointer Networks Jian Li, Yue Wang, Michael R. Lyu, Irwin King
• Pythia: AI-assisted Code Completion System Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, Neel Sundaresan
• Learning Autocompletion from Real-World Datasets Gareth Ari Aye, Seohyun Kim, Hongyu Li
• Sequence Model Design for Code Completion in the Modern IDE Gareth Ari Aye, Gail E. Kaiser
• Code Prediction by Feeding Trees to Transformers Seohyun Kim, Jinman Zhao, Yuchi Tian, Satish Chandra
• On-the-Fly Adaptation of Source Code Models using Meta-Learning Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
• A Structural Model for Contextual Code Changes Shaked Brody, Uri Alon, Eran Yahav
• IntelliCode Compose: Code Generation Using Transformer Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan
• Fast and Memory-Efficient Neural Code Completion Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Franco, Miltiadis Allamanis
• Suggesting Comment Completions for Python using Neural Language Models Adelina Ciurumelea; Sebastian Proksch; Harald C. Gall
• Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
• Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
• Improving Code Autocompletion with Transfer Learning Wen Zhou, Seohyun Kim, Vijayaraghavan Murali, Gareth Ari Aye
• You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion Roei Schuster, Congzheng Song, Eran Tromer, Vitaly Shmatikov
• On the Embeddings of Variables in Recurrent Neural Networks for Source Code Nadezhda Chirkova
• ReACC: A Retrieval-Augmented Code Completion Framework Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, Alexey Svyatkovskiy
• All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs Vitaliy Bibaev, Alexey Kalina, Vadim Lomshakov, Yaroslav Golubev, Alexander Bezzubov, Nikita Povarov, Timofey Bryksin
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani

🏷 benchmark

• ConTest: A Unit Test Completion Benchmark featuring Context Johannes Villmow, Jonas Depoix, Adrian Ulges
• CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu
• Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani

🏷 benchmarking

• PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models Simin Chen, Xiaoning Feng, Xiaohong Han, Cong Liu, Wei Yang

🏷 bimodal

• Natural Language Models for Predicting Programming Comments Dana Movshovitz-Attias, William W. Cohen
• Using Semantic Unification to Generate Regular Expressions from Natural Language Nate Kushman, Regina Barzilay
• NLyze: Interactive Programming by Natural Language for SpreadSheet Data Analysis and Manipulation Sumit Gulwani, Mark Marron
• Synthesizing Java expressions from free-form queries Tihomir Gvero, Viktor Kuncak
• A Bimodal Modelling of Source Code and Natural Language Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
• Learning to Generate Pseudo-code from Source Code using Statistical Machine Translation Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura
• Summarizing Source Code using a Neural Attention Model Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• Latent Predictor Networks for Code Generation Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
• CodeSum: Translate Program Language to Natural Language Xing Hu, Yuhan Wei, Ge Li, Zhi Jin
• Function Assistant: A Tool for NL Querying of APIs Kyle Richardson, Jonas Kuhn
• Automatically Generating Commit Messages from Diffs using Neural Machine Translation Siyuan Jiang, Ameer Armaly, Collin McMillan
• The Code2Text Challenge: Text Generation in Source Code Libraries Kyle Richardson, Sina Zarrieß, Jonas Kuhn
• Learning Technical Correspondences in Technical Documentation Kyle Richardson, Jonas Kuhn
• Program Synthesis from Natural Language Using Recurrent Neural Networks Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, Michael D. Ernst
• A Syntactic Neural Model for General-Purpose Code Generation Pengcheng Yin, Graham Neubig
• pix2code: Generating Code from a Graphical User Interface Screenshot Tony Beltramelli
• A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
• NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, Michael D. Ernst
• Deep Learning to Detect Redundant Method Comments Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
• Polyglot Semantic Parsing in APIs Kyle Richardson, Jonathan Berant, Jonas Kuhn
• Mapping Language to Code in Programmatic Context Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• Generating Regular Expressions from Natural Language Specifications: Are We There Yet? Zexuan Zhong, Jiaqi Guo, Wei Yang, Tao Xie, Jian-Guang Lou, Ting Liu, Dongmei Zhang
• SPoC: Search-based Pseudocode to Code Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy S. Liang
• TypeWriter: Neural Type Prediction with Search-based Validation Michael Pradel, Georgios Gousios, Jason Liu, Satish Chandra.
• Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially Yudong Zhang, Wenhao Zheng, Ming Li
• NL2Type: Inferring JavaScript Function Types from Natural Language Information Rabee Sohail Malik, Jibesh Patra, Michael Pradel
• JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer
• OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton
• Incorporating External Knowledge through Pre-training for Natural Language to Code Generation Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, Graham Neubig
• Learning to Update Natural Language Comments Based on Code Changes Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• Associating Natural Language Comment and Source Code Entities Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• Code to Comment "Translation": Data, Metrics, Baselining & Evaluation David Gros, Hariharan Sezhiyan, Premkumar Devanbu, Zhou Yu
• TAG : Type Auxiliary Guiding for Code Comment Generation Ruichu Cai, Zhihao Liang, Boyan Xu, Zijian Li, Yuexing Hao, Yao Chen
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
• Where should I comment my code? A dataset and model for predicting locations that need comments Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
• Suggesting Comment Completions for Python using Neural Language Models Adelina Ciurumelea; Sebastian Proksch; Harald C. Gall
• Co-Training for Commit Classification Jian Yi, David Lee, Hai Leong Chieu

🏷 Binary Code

• Learning to Reverse DNNs from AI Programs Automatically Simin Chen, Hamed Khanpour, Cong Liu, Wei Yang

🏷 clone

• Deep Learning Code Fragments for Code Clone Detection Martin White, Michele Tufano, Christopher Vendome, Denys Poshyvanyk.
• Deep Learning Similarities from Different Representations of Source Code Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Oreo: detection of clones in the twilight zone Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Pierre Baldi, Cristina Lopes
• Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization Steven H. H. Ding, Benjamin C. M. Fung, Philippe Charland
• Learning-based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection Lutz Büch, Artur Andrzejak
• funcGNN: A Graph Neural Network Approach to Program Similarity Aravind Nair, Avijit Roy, Karl Meinke
• Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
• Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree Wenhan Wang, Ge Li, Bo Ma, Xin Xia, Zhi Jin
• Cross-Language Binary-Source Code Matching with Intermediate Representations Yi Gui, Yao Wan, Hongyu Zhang, Huifang Huang, Yulei Sui, Guandong Xu, Zhiyuan Shao, Hai Jin
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo

🏷 code completion

• Repository-Level Prompt Generation for Large Language Models of Code Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani

🏷 code generation

• A Machine Learning Framework for Programming by Example Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, Adam Kalai
• Using Semantic Unification to Generate Regular Expressions from Natural Language Nate Kushman, Regina Barzilay
• Structured Generative Models of Natural Source Code Chris J. Maddison, Daniel Tarlow
• Phrase-Based Statistical Translation of Programming Languages S. Karaivanov, Veselin Raychev, Martin Vechev
• Code Completion with Statistical Language Models Veselin Raychev, Martin Vechev, Eran Yahav
• NLyze: Interactive Programming by Natural Language for SpreadSheet Data Analysis and Manipulation Sumit Gulwani, Mark Marron
• Synthesizing Java expressions from free-form queries Tihomir Gvero, Viktor Kuncak
• Visualizing and Understanding Recurrent Networks Andrej Karpathy, Justin Johnson, Li Fei-Fei
• Learning Programs from Noisy Data Veselin Raychev, Pavol lBielik, Martin Vechev, Andreas Krause
• PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
• Latent Predictor Networks for Code Generation Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
• A deep language model for software code Hoa Khanh Dam, Truyen Tran, Trang Pham
• Neural Attribute Machines for Program Generation Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
• Program Synthesis from Natural Language Using Recurrent Neural Networks Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, Michael D. Ernst
• A Syntactic Neural Model for General-Purpose Code Generation Pengcheng Yin, Graham Neubig
• Abstract Syntax Networks for Code Generation and Semantic Parsing Maxim Rabinovich, Mitchell Stern, Dan Klein
• DeepFix: Fixing Common C Language Errors by Deep Learning Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade
• Synthesizing benchmarks for predictive modeling Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather
• pix2code: Generating Code from a Graphical User Interface Screenshot Tony Beltramelli
• A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
• NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, Michael D. Ernst
• Compiler Fuzzing through Deep Learning Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather
• Deep Reinforcement Learning for Programming Language Correction Rahul Gupta, Aditya Kanade, Shirish Shevade
• Mapping Language to Code in Programmatic Context Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• Generating Regular Expressions from Natural Language Specifications: Are We There Yet? Zexuan Zhong, Jiaqi Guo, Wei Yang, Tao Xie, Jian-Guang Lou, Ting Liu, Dongmei Zhang
• Learning to Repair Software Vulnerabilities with Generative Adversarial Networks Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin
• Learning to Generate Corrective Patches using Neural Machine Translation Hideaki Hata, Emad Shihab, Graham Neubig
• CODIT: Code Editing with Tree-Based Neural Machine Translation Saikat Chakraborty, Miltiadis Allamanis, Baishakhi Ray
• Bayesian Sketch Learning for Program Synthesis Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
• SampleFix: Learning to Correct Programs by Sampling Diverse Fixes Hossein Hajipour, Apratim Bhattacharyya, Cristian-Alexandru Staicu, Mario Fritz
• Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
• Structural Language Models for Any-Code Generation Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav
• A Grammar-Based Structural CNN Decoder for Code Generation Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang
• Code Generation as a Dual Task of Code Summarization Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
• DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu
• SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus
• A case study on machine learning for synthesizing benchmarks Andrés Goens, Alexander Brauckmann, Sebastian Ertel, Chris Cummins, Hugh Leather, Jeronimo Castrillon
• Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
• Incorporating External Knowledge through Pre-training for Natural Language to Code Generation Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, Graham Neubig
• Semantic Scaffolds for Pseudocode-to-Code Generation Ruiqi Zhong, Mitchell Stern, Dan Klein
• Unit Test Case Generation with Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
• IntelliCode Compose: Code Generation Using Transformer Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan
• Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• Energy-Based Models for Code Generation under Compilability Constraints Tomasz Korbak, Hady Elsahar, Marc Dymetman, Germán Kruszewski
• Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
• Time-Efficient Code Completion Model for the R Programming Language Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev
• Shellcode_IA32: A Dataset for Automatic Shellcode Generation Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella, Bojan Cukic, Samira Shaikh
• TOGA: A Neural Method for Test Oracle Generation Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, Shuvendu K. Lahiri
• InCoder: A Generative Model for Code Infilling and Synthesis Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis
• DocCoder: Generating Code by Retrieving and Reading Docs Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao JIang, Graham Neubig
• Human perceiving behavior modeling in evaluation of code generation models S. Kovalchuk, V. Lomshakov, A. Aliev
• Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models Priyan Vaithilingam, Tianyi Zhang, Elena Glassman
• Open-ended Knowledge Tracing Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew Lan
• Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani

🏷 code similarity

• MISIM: An End-to-End Neural Code Similarity System Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
• Senatus - A Fast and Accurate Code-to-Code Recommendation Engine Fran Silavong, Sean Moran, Antonios Georgiadis, Rohan Saphal, Robert Otter
• Cross-Language Binary-Source Code Matching with Intermediate Representations Yi Gui, Yao Wan, Hongyu Zhang, Huifang Huang, Yulei Sui, Guandong Xu, Zhiyuan Shao, Hai Jin
• CV4Code: Sourcecode Understanding via Visual Code Representations Ruibo Shi, Lili Tao, Rohan Saphal, Fran Silavong, Sean J. Moran

🏷 compilation

• DeepDelta: Learning to Repair Compilation Errors Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.
• A Neural Approach to Decompiled Identifier Renaming Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
• Static Neural Compiler Optimization via Deep Reinforcement Learning Rahim Mammadli, Ali Jannesari, Felix Wolf
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani

🏷 completion

• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
• RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
• RepoFusion: Training Code Models to Understand Your Repository Disha Shrivastava, Denis Kocetkov, Harm de Vries, Dzmitry Bahdanau, Torsten Scholak

🏷 cybersecurity

• A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks Beatrice Casey, Joanna C. S. Santos, George Perry

🏷 dataset

• A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
• Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow Pengcheng Yin, B. Deng, E. Chen, B. Vasilescu, Graham Neubig
• Public Git Archive: a Big Code dataset for all Vadim Markovtsev, Waren Long
• StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow Ziyu Yao, Daniel S. Weld, Wei-Peng Chen, Huan Sun
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt
• Neural Code Search Evaluation Dataset Hongyu Li, Seohyun Kim, Satish Chandra
• Recommendations for Datasets for Source Code Summarization Alexander LeClair, Collin McMillan
• JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer
• The Adverse Effects of Code Duplication in Machine Learning Models of Code Miltiadis Allamanis
• Graph4Code: A Machine Interpretable Knowledge Graph for Code Ibrahim Abdelaziz, Julian Dolby, James P. McCusker, Kavitha Srinivas
• Associating Natural Language Comment and Source Code Entities Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• Code and Named Entity Recognition in StackOverflow Jeniya Tabassum, Mounica Maddela, Wei Xu, Alan Ritter
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather
• Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size Martin Monperrus, Matias Martinez, He Ye, Fernanda Madeiral, Thomas Durieux, Zhongxing Yu
• CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
• CoSQA: 20,000+ Web Queries for Code Search and Question Answering Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan
• ConTest: A Unit Test Completion Benchmark featuring Context Johannes Villmow, Jonas Depoix, Adrian Ulges
• A large-scale benchmark for few-shot program induction and synthesis Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, Joshua Tenenbaum
• Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation Gabriel Orlanski, Alex Gittens
• Time-Efficient Code Completion Model for the R Programming Language Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev
• ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference Amir M. Mir, Evaldas Latoskinas, Georgios Gousios
• Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks Ruchir Puri, David S. Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Ulrich Finkler
• Shellcode_IA32: A Dataset for Automatic Shellcode Generation Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella, Bojan Cukic, Samira Shaikh
• Impact of Evaluation Methodologies on Code Summarization Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, Milos Gligoric
• Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data Moshe Hazoom, Vibhor Malik, Ben Bogin
• The Stack: 3TB of permissively licensed source code Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, Harm de Vries
• Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow
• Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
• JEMMA: An Extensible Java Dataset for ML4Code Applications Anjan Karmakar, Miltiadis Allamanis, Romain Robbes
• OctoPack: Instruction Tuning Code Large Language Models Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
• DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection Yizheng Chen, Zhoujie Ding, Xinyun Chen, David Wagner

🏷 decompilation

• Learning to Align the Source Code to the Compiled Object Code Dor Levy, Lior Wolf
• Towards Neural Decompilation Omer Katz, Yuval Olshaker, Yoav Goldberg, Eran Yahav
• Coda: An End-to-End Neural Program Decompiler Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao
• DIRECT : A Transformer-based Model for Decompiled Identifier Renaming Vikram Nitin, Anthony Saieva, Baishakhi Ray, Gail Kaiser
• Code Translation with Compiler Representations Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, Gabriel Synnaeve
• LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang

🏷 defect

• Using Web Corpus Statistics for Program Analysis Chun-Hung Hsiao, Michael Cafarella, Satish Narayanasamy
• On the “Naturalness” of Buggy Code Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, Premkumar Devanbu
• Bugram: bug detection with n-gram language models Song Wang, Devin Chollak, Dana Movshovitz-Attias, Lin Tan
• Automatically Learning Semantic Features for Defect Prediction Song Wang, Taiyue Liu, Lin Tan
• Deep Learning to Find Bugs Michael Pradel, Koushik Sen
• Software Defect Prediction via Convolutional Neural Network Jian Li, Pinjia He, Jieming Zhu, Michael R. Lyu
• Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
• Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
• Exploring the Naturalness of Buggy Code with Recurrent Neural Network Jack Lanchantin, Ji Gao
• Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks Yi Li, Shaohua Wang, Tien N. Nguyen, Son Van Nguyen
• Scalable Taint Specification Inference with Big Code V. Chibotaru, B. Bichsel, Veselin Raychev, Martin Vechev
• Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta, Aditya Kanade, Shirish Shevade
• Learning Semantic Program Embeddings with Graph Interval Neural Network Yu Wang, Fengjuan Gao, Linzhang Wang, Ke Wang
• OffSide: Learning to Identify Mistakes in Boundary Conditions Jón Arnar Briem, Jordi Smit, Hendrig Sellik, Pavel Rapoport, Georgios Gousios, Maurício Aniche.
• Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
• SCELMo: Source Code Embeddings from Language Models Rafael-Michael Karampatsis, Charles Sutton
• Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
• Co-Training for Commit Classification Jian Yi, David Lee, Hai Leong Chieu
• Deep Learning based Vulnerability Detection: Are We There Yet? Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, Baishakhi Ray
• On Distribution Shift in Learning-based Bug Detectors Jingxuan He, Luca Beurer-Kellner, Martin Vechev
• Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow
• Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes Cedric Richter, Heike Wehrheim
• Large Language Models and Simple, Stupid Bugs Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu, Emily Morgan

🏷 deobfuscation

• Predicting Program Properties from “Big Code” Veselin Raychev, Martin Vechev, Andreas Krause
• Statistical Deobfuscation of Android Applications Benjamin Bichsel, Veselin Raychev, Petar Tsankov, Martin Vechev
• Towards Better Program Obfuscation: Optimization via Language Models Han Liu
• Recovering Clear, Natural Identifiers from Obfuscated JS Names Bogdan Vasilescu, Casey Casalnuovo, Premkumar Devanbu
• Recovering Variable Names for Minified Code with Usage Contexts Hieu Tran, Ngoc Tran, Son Nguyen, Hoan Nguyen, Tien N. Nguyen
• A Neural Approach to Decompiled Identifier Renaming Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
• Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav

🏷 documentation

• Natural Language Models for Predicting Programming Comments Dana Movshovitz-Attias, William W. Cohen
• A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
• Learning Technical Correspondences in Technical Documentation Kyle Richardson, Jonas Kuhn
• Deep Learning to Detect Redundant Method Comments Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
• Improving Automatic Source Code Summarization via Deep Reinforcement Learning Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu
• Structured Neural Summarization Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
• A Neural Model for Generating Natural Language Summaries of Program Subroutines Alexander LeClair, Siyuan Jiang, Collin McMillan
• Learning to Update Natural Language Comments Based on Code Changes Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• Code to Comment "Translation": Data, Metrics, Baselining & Evaluation David Gros, Hariharan Sezhiyan, Premkumar Devanbu, Zhou Yu
• TAG : Type Auxiliary Guiding for Code Comment Generation Ruichu Cai, Zhihao Liang, Boyan Xu, Zijian Li, Yuexing Hao, Yao Chen
• TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search Wenhua Wang, Yuqun Zhang, Zhengran Zeng, Guandong Xu
• Automating Just-In-Time Comment Updating Zhongxin Liu, Xin Xia, Meng Yan, Shanping Li
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
• Where should I comment my code? A dataset and model for predicting locations that need comments Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
• Suggesting Comment Completions for Python using Neural Language Models Adelina Ciurumelea; Sebastian Proksch; Harald C. Gall
• NaturalCC: A Toolkit to Naturalize the Source Code Corpus Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu
• Learning to Describe Solutions for Bug Reports Based on Developer Discussions Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• Assemble Foundation Models for Automatic Code Summarization Jian Gu, Pasquale Salza, Harald C. Gall
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard

🏷 dynamic

• Learning Scalable and Precise Representation of Program Semantics Ke Wang
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
• Blended, precise semantic program embeddings Ke Wang, Zhendong Su
• TraceFixer: Execution Trace-Driven Program Repair Islem Bouzenia, Yangruibo Ding, Kexin Pei, Baishakhi Ray, Michael Pradel
• Predictive Program Slicing via Execution Knowledge-Guided Dynamic Dependence Learning Aashish Yadavally, Yi Li, Tien N. Nguyen

🏷 edit

• A Study of Repetitiveness of Code Changes in Software Evolution Hoan Anh Nguyen, Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen, and Hridesh Rajan
• Automatically Generating Commit Messages from Diffs using Neural Machine Translation Siyuan Jiang, Ameer Armaly, Collin McMillan
• A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes Pablo Loyola, Edison Marrese-Taylor, Yutaka Matsuo
• Learning How to Mutate Source Code from Bug-Fixes Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Content Aware Source Code Change Description Generation Pablo Loyola, Edison Marrese-Taylor, Jorge Balazs, Yutaka Matsuo, Fumiko Satoh
• Neural-Machine-Translation-Based Commit Message Generation: How Far Are We? Zhongxin Liu, Xin Xia, Ahmed E. Hassan, David Lo, Zhenchang Xing, Xinyu Wang
• Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change Patterns Hoan Anh Nguyen, Tien N. Nguyen, Danny Dig, Son Nguyen, Hieu Tran, and Michael Hilton
• On Learning Meaningful Code Changes via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Learning to Fix Build Errors with Graph2Diff Neural Networks Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
• Commit Message Generation for Source Code Changes Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, Jian Lu
• Learning to Represent Edits Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
• DeepDelta: Learning to Repair Compilation Errors Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.
• Generating commit messages from diffs using pointer-generator network Qin Liu, Zihe Liu, Hongming Zhu, Hongfei Fan, Bowen Du, Yu Qian.
• Commit2Vec: Learning Distributed Representations of Code Changes Adelina Ciurumelea; Sebastian Proksch; Harald C. Gall
• Neural Networks for Modeling Source Code Edits Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow
• Hoppity: Learning Bug Detection and Repair Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang
• Copy that! Editing Sequences by Copying Spans Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt
• Learning to Update Natural Language Comments Based on Code Changes Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• CC2Vec: Distributed Representations of Code Changes Thong Hoang, Hong Jin Kang, Julia Lawall, David Lo
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• A Structural Model for Contextual Code Changes Shaked Brody, Uri Alon, Eran Yahav
• DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
• Unsupervised Learning of General-Purpose Embeddings for Code Changes Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev, Timofey Bryksin
• Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size Martin Monperrus, Matias Martinez, He Ye, Fernanda Madeiral, Thomas Durieux, Zhongxing Yu
• A Semantic Bug Seeding: A Learning-Based Approach for Creating Realistic Bugs Jibesh Patra, Michael Pradel
• Jointly Learning to Repair Code and Generate Commit Message Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li
• DeepMerge: Learning to Merge Programs Elizabeth Dinella, Todd Mytkowicz, Alexey Svyatkovskiy, Christian Bird, Mayur Naik, Shuvendu K. Lahiri
• On Multi-Modal Learning of Editing Source Code Saikat Chakraborty, Baishakhi Ray
• A Syntax-Guided Edit Decoder for Neural Program Repair Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, Lu Zhang
• Learning to Model Editing Processes Machel Reid, Graham Neubig
• CoditT5: Pretraining for Source Code and Natural Language Editing Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Jessy Li, Milos Gligoric

🏷 editing

• Grace: Language Models Meet Code Edits Priyanshu Gupta, Avishree Khare, Yasharth Bajpai, Saikat Chakraborty, Sumit Gulwani, Aditya Kanade, Arjun Radhakrishna, Gustavo Soares, Ashish Tiwari
• Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions Federico Cassano, Luisa Li, Akul Sethi, Noah Shinn, Abby Brennan-Jones, Jacob Ginesin, Edward Berman, George Chakhnashvili, Anton Lozhkov, Carolyn Jane Anderson, Arjun Guha

🏷 education

• A system to grade computer programming skills using machine learning Shashank Srikant, Varun Aggarwal
• Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
• Question Independent Grading using Machine Learning: The Case of Computer Program Grading Gursimran Singh, Shashank Srikant, Varun Aggarwal
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback Mike Wu, Noah D. Goodman, Chris Piech, Chelsea Finn
• Open-ended Knowledge Tracing Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew Lan

🏷 evaluation

• Testing Neural Program Analyzers Md Rafiqul Islam Rabin, Ke Wang, Mohammad Amin Alipour
• The Adverse Effects of Code Duplication in Machine Learning Models of Code Miltiadis Allamanis
• Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
• CodeBLEU: a Method for Automatic Evaluation of Code Synthesis Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, Shuai Ma
• On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
• Impact of Evaluation Methodologies on Code Summarization Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, Milos Gligoric
• Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
• CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code Aryaz Eghbali, Michael Pradel
• Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
• Semantic Similarity Metrics for Evaluating Source Code Summarization Sakib Haque, Zachary Eberhart, Aakash Bansal, Collin McMillan
• Human perceiving behavior modeling in evaluation of code generation models S. Kovalchuk, V. Lomshakov, A. Aliev
• An Extensive Study on Pre-trained Models for Program Understanding and Generation Zhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li, Yuqun Zhang, Lingming Zhang
• Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
• Natural Language to Code Generation in Interactive Data Science Notebooks Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton
• Productivity Assessment of Neural Code Completion Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew Rice, Devon Rifkin, Edward Aftandilian
• CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code Shuyan Zhou, Uri Alon, Sumit Agarwal, Graham Neubig
• Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
• CodeScore: Evaluating Code Generation by Learning Code Execution Yihong Dong, Jiazheng Ding, Xue Jiang, Zhuo Li, Ge Li, Zhi Jin
• PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models Simin Chen, Xiaoning Feng, Xiaohong Han, Cong Liu, Wei Yang
• LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang

🏷 execution

• Learning to Execute Wojciech Zaremba, Ilya Sutskever
• Show Your Work: Scratchpads for Intermediate Computation with Language Models Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena
• SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics He Ye, Matias Martinez, Xiapu Luo, Tao Zhang, Martin Monperrus
• CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
• Code Execution with Pre-trained Language Models Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan
• LExecutor: Learning-Guided Execution Beatriz Souza, Michael Pradel

🏷 feature location

• Exploring the Use of Deep Learning for Feature Location Christopher S. Corley, Kostadin Damevski, Nicholas A. Kraft

🏷 fuzzing

• Learning to Fuzz: Application-Independent Fuzz Testing with Probabilistic, Generative Models of Input Data Jibesh Patra, Michael Pradel
• Compiler Fuzzing through Deep Learning Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather
• NEUZZ: Efficient Fuzzing with Neural Program Smoothing Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana
• DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu
• Learning to Fuzz from Symbolic Execution with Application to Smart Contracts Jingxuan He, Mislav Balunović, Nodar Ambroladze, Petar Tsankov, Martin Vechev
• Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer Suyoung Lee, HyungSeok Han, Sang Kil Cha, Sooel Son
• Universal Fuzzing via Large Language Models Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian, Michael Pradel, Lingming Zhang

🏷 generalizability

• On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
• Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
• Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff

🏷 generation

• Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation Xin-Ye Li, Jiang-Tian Xue, Zheng Xie, Ming Li

🏷 GNN

• Gated Graph Sequence Neural Networks Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
• Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
• Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
• Simulating Execution Time of Tensor Programs using Graph Neural Networks Jakub M. Tomczak, Romain Lepert, Auke Wiggers
• Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
• Using GGNN to recommend log statement level Mingzhe Li, Jianrui Pei, Jin He, Kevin Song, Frank Che, Yongfeng Huang, Chitai Wang
• Structured Neural Summarization Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
• Program Classification Using Gated Graph Attention Neural Network for Online Programming Service Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
• AutoPandas: neural-backed generators for program synthesis Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica
• Learning to Fuzz from Symbolic Execution with Application to Smart Contracts Jingxuan He, Mislav Balunović, Nodar Ambroladze, Petar Tsankov, Martin Vechev
• Inferring Javascript types using Graph Neural Networks Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
• Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav
• Learning Semantic Program Embeddings with Graph Interval Neural Network Yu Wang, Fengjuan Gao, Linzhang Wang, Ke Wang
• LambdaNet: Probabilistic Type Inference using Graph Neural Networks Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
• Typilus: Neural Type Hints Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
• Learning Graph Structure With A Finite-State Automaton Layer Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
• Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather
• Towards Learning Representations of Binary Executable Files for Security Tasks Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan
• funcGNN: A Graph Neural Network Approach to Program Similarity Aravind Nair, Avijit Roy, Karl Meinke
• Deep Graph Matching and Searching for Semantic Code Retrieval Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu
• Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
• Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree Wenhan Wang, Ge Li, Bo Ma, Xin Xia, Zhi Jin
• Learning to Represent Programs with Heterogeneous Graphs Wenhan Wang, Kechi Zhang, Ge Li, Zhi Jin
• Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt

🏷 grammar

• Structured Statistical Syntax Tree Prediction Cyrus Omar
• Structured Generative Models of Natural Source Code Chris J. Maddison, Daniel Tarlow
• Mining Idioms from Source Code Miltiadis Allamanis, Charles Sutton
• Building Program Vector Representations for Deep Learning Hao Peng, Lili Mou, Ge Li, Yuxuan Liu, Lu Zhang, Zhi Jin.
• A Bimodal Modelling of Source Code and Natural Language Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
• Learning to Generate Pseudo-code from Source Code using Statistical Machine Translation Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura
• Learning Programs from Noisy Data Veselin Raychev, Pavol lBielik, Martin Vechev, Andreas Krause
• PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
• Convolutional Neural Networks over Tree Structures for Programming Language Processing Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin
• Neural Attribute Machines for Program Generation Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
• Mining Semantic Loop Idioms from Big Code Miltiadis Allamanis, Earl T. Barr, Christian Bird, Mark Marron, Charles Sutton
• A Syntactic Neural Model for General-Purpose Code Generation Pengcheng Yin, Graham Neubig
• Abstract Syntax Networks for Code Generation and Semantic Parsing Maxim Rabinovich, Mitchell Stern, Dan Klein
• Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks Nghi D. Q. Bui, Lingxiao Jiang, Yijun Yu
• CODIT: Code Editing with Tree-Based Neural Machine Translation Saikat Chakraborty, Miltiadis Allamanis, Baishakhi Ray
• Capturing source code semantics via tree-based convolution over API-enhanced AST Long Chen, Wei Ye, Shikun Zhang
• Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
• Automatic Source Code Summarization with Extended Tree-LSTM Yusuke Shido, Yasuaki Kobayashi, Akihiro Yamamoto, Atsushi Miyamoto, Tadayuki Matsumura
• A Grammar-Based Structural CNN Decoder for Code Generation Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang
• PathMiner : A Library for Mining of Path-Based Representations of Code Vladimir Kovalenko, Egor Bogomolov, Timofey Bryksin, Alberto Bacchelli.
• Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
• Learning-based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection Lutz Büch, Artur Andrzejak
• Neural-Network Guided Expression Transformation Romain Edelmann, Viktor Kunčak
• A Novel Neural Source Code Representation based on Abstract Syntax Tree Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu
• PSCS: A Path-based Neural Model for Semantic Code Search Zhensu Sun, Yan Liu, Chen Yang, Yu Qian
• Modular Tree Network for Source Code Representation Learning Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, Zhi Jin
• A Structural Model for Contextual Code Changes Shaked Brody, Uri Alon, Eran Yahav
• Predicting Vulnerability in Large Codebases With Deep Code Representation Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi
• DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
• TreeBERT: A Tree-Based Pre-Trained Model for Programming Language Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, Lei Lyu
• Learning to Complete Code with Sketches Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis

🏷 human evaluation

• Grounded Copilot: How Programmers Interact with Code-Generating Models Shraddha Barke, Michael B. James, Nadia Polikarpova
• Semantic Similarity Metrics for Evaluating Source Code Summarization Sakib Haque, Zachary Eberhart, Aakash Bansal, Collin McMillan
• Human perceiving behavior modeling in evaluation of code generation models S. Kovalchuk, V. Lomshakov, A. Aliev
• Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models Priyan Vaithilingam, Tianyi Zhang, Elena Glassman
• What is it like to program with artificial intelligence? Advait Sarkar, Andrew D. Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, Ben Zorn
• Productivity Assessment of Neural Code Completion Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew Rice, Devon Rifkin, Edward Aftandilian

🏷 information extraction

• A Hidden Markov Model to Detect Coded Information Islands in Free Text Luigi Cerulo, Michele Ceccarelli, Massimiliano Di Penta, Gerardo Canfora
• Irish: A Hidden Markov Model to detect coded information islands in free text Luigi Cerulo, Michele Ceccarelli, Massimiliano Di Penta, Gerardo Canfora
• NIRMAL: Automatic Identification of Software Relevant Tweets Leveraging Language Model Abhishek Sharma, Yuan Tian, David Lo
• Extracting Code from Programming Tutorial Videos Shir Yadid, Eran Yahav
• Evaluation of Type Inference with Textual Cues Amirreza A. Shirani, A. Pastor Lopez-Monroy, Fabio Gonzalez, Thamar Solorio, Mohammad Amin Alipour
• A Deep Learning Approach to Identifying Source Code in Images and Video Jordan Ott, Abigail Atchison, Paul Harnack, Adrienne Bergh, Erik Linstead.
• Code and Named Entity Recognition in StackOverflow Jeniya Tabassum, Mounica Maddela, Wei Xu, Alan Ritter
• Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour

🏷 instruction tuning

• OctoPack: Instruction Tuning Code Large Language Models Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre

🏷 interpretability

• Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
• Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour
• Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
• Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo

🏷 language model

• On the Naturalness of Software Abram Hindle, Earl T. Barr, Mark Gabel, Zhendong Su, Premkumar Devanbu
• Mining Source Code Repositories at Massive Scale Using Language Modeling Miltiadis Allamanis, Charles Sutton
• Structured Statistical Syntax Tree Prediction Cyrus Omar
• A Statistical Semantic Language Model for Source Code Tung Thanh Nguyen, Anh Tuan Nguyen, Hoan Anh Nguyen, Tien N. Nguyen
• Structured Generative Models of Natural Source Code Chris J. Maddison, Daniel Tarlow
• Learning Natural Coding Conventions Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
• On the Localness of Software Zhaopeng Tu, Zhendong Su, Premkumar Devanbu
• Code Completion with Statistical Language Models Veselin Raychev, Martin Vechev, Eran Yahav
• Syntax Errors Just Aren’t Natural: Improving Error Reporting with Language Models Joshua Charles Campbell, Abram Hindle, José Nelson Amaral
• CACHECA: A Cache Language Model Based Code Suggestion Tool Christine Franks, Zhaopeng Tu, Premkumar Devanbu, Vincent Hellendoorn
• Graph-based Statistical Language Model for Code Anh Tuan Nguyen, Tien N. Nguyen
• Visualizing and Understanding Recurrent Networks Andrej Karpathy, Justin Johnson, Li Fei-Fei
• Products, Developers, and Milestones: How Should I Build My N-Gram Language Model Juliana Saraiva, Christian Bird, Thomas Zimmermann
• Will they like this? Evaluating Code Contributions With Language Models Vincent J. Hellendoorn, Premkumar Devanbu, Alberto Bacchelli
• PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
• Learning Python Code Suggestion with a Sparse Pointer Network Avishkar Bhoopchand, Tim Rocktaschel, Earl Barr, Sebastian Riedel
• A deep language model for software code Hoa Khanh Dam, Truyen Tran, Trang Pham
• A Language Model for Statements of Software Code Yixiao Yang, Yu Jiang, Ming Gu, Jiaguang Sun, Jian Gao, Han Liu
• Are Deep Neural Networks the Best Choice for Modeling Source Code? Vincent J. Hellendoorn, Premkumar Devanbu
• Code Completion with Neural Attention and Pointer Networks Jian Li, Yue Wang, Michael R. Lyu, Irwin King
• Building Language Models for Text with Named Entities M.R. Parvez, Saikat Chakraborty, Baishakhi Ray, KW Chang
• Exploring the Naturalness of Buggy Code with Recurrent Neural Network Jack Lanchantin, Ji Gao
• Syntax and Sensibility: Using language models to detect and correct syntax errors Eddie Antonio Santos, Joshua Charles Campbell, Dhvani Patel, Abram Hindle, José Nelson Amaral
• On the Impact of Refactoring Operations on Code Naturalness Bin Lin, Csaba Nagy, Gabriele Bavota, Michele Lanza
• Maybe Deep Neural Networks are the Best Choice for Modeling Source Code Rafael-Michael Karampatsis, Charles Sutton
• Pythia: AI-assisted Code Completion System Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, Neel Sundaresan
• On-the-Fly Adaptation of Source Code Models using Meta-Learning Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
• Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes Charles Sutton, Andrea Janes
• PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
• Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer Suyoung Lee, HyungSeok Han, Sang Kil Cha, Sooel Son
• IntelliCode Compose: Code Generation Using Transformer Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan
• CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
• Evaluating Large Language Models Trained on Code Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, Will Guss, Alex Nichol, Igor Babuschkin, Suchir Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba
• Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
• An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri
• Capturing Structural Locality in Non-parametric Language Models Frank F. Xu, Junxian He, Graham Neubig, Vincent J. Hellendoorn
• Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
• Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
• Time-Efficient Code Completion Model for the R Programming Language Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev
• On the Naturalness and Localness of Software Logs Sina Gholamian, Paul A. S. Ward
• Neural Program Generation Modulo Static Analysis Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Chris Jermaine
• Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
• Efficient Training of Language Models to Fill in the Middle Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen
• Assemble Foundation Models for Automatic Code Summarization Jian Gu, Pasquale Salza, Harald C. Gall
• A Systematic Evaluation of Large Language Models of Code Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn
• Synchromesh: Reliable code generation from pre-trained language models Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani
• Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
• Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, Xiangke Liao
• Learning to Complete Code with Sketches Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis
• Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
• Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models Priyan Vaithilingam, Tianyi Zhang, Elena Glassman
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani

🏷 large language models

• Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets V. Lomshakov, S. Kovalchuk, M. Omelchenko, S. Nikolenko, A. Aliev
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
• (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
• LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang
• Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search Haochen Li, Xin Zhou, Zhiqi Shen
• A Learning-Based Approach to Static Program Slicing Aashish Yadavally, Yi Li, Shaohua Wang, Tien N. Nguyen
• Predictive Program Slicing via Execution Knowledge-Guided Dynamic Dependence Learning Aashish Yadavally, Yi Li, Tien N. Nguyen

🏷 LLM

• A Static Evaluation of Code Completion by Large Language Models Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
• LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang

🏷 logging

• Using GGNN to recommend log statement level Mingzhe Li, Jianrui Pei, Jin He, Kevin Song, Frank Che, Yongfeng Huang, Chitai Wang
• On the Naturalness and Localness of Software Logs Sina Gholamian, Paul A. S. Ward
• Using Deep Learning to Generate Complete Log Statements Antonio Mastropaolo, Luca Pascarella, Gabriele Bavota

🏷 memorization

• Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn

🏷 metrics

• Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev
• Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search Haochen Li, Xin Zhou, Zhiqi Shen

🏷 migration

• Lexical Statistical Machine Translation for Language Migration Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen
• Phrase-Based Statistical Translation of Programming Languages S. Karaivanov, Veselin Raychev, Martin Vechev
• Statistical Learning Approach for Mining API Usage Mappings for Code Migration Anh Tuan Nguyen, Hoan Anh Nguyen, Tung Thanh Nguyen, Tien N. Nguyen
• Divide-and-Conquer Approach for Multi-phase Statistical Migration for Source Code Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen
• Using Machine Translation for Converting Python 2 to Python 3 Code Karan Aggarwal, Mohammad Salameh, Abram Hindle
• Mapping API Elements for Code Migration with Vector Representations Trong Duc Nguyen, Anh Tuan Nguyen, Tien N. Nguyen
• Unsupervised Translation of Programming Languages Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, Guillaume Lample
• Leveraging Automated Unit Tests for Unsupervised Code Translation Baptiste Roziere, Jie M. Zhang, Francois Charton, Mark Harman, Gabriel Synnaeve, Guillaume Lample
• Code Translation with Compiler Representations Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, Gabriel Synnaeve

🏷 naming

• Learning Natural Coding Conventions Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
• Predicting Program Properties from “Big Code” Veselin Raychev, Martin Vechev, Andreas Krause
• Suggesting Accurate Method and Class Names Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
• A Convolutional Attention Network for Extreme Summarization of Source Code Miltiadis Allamanis, Hao Peng, Charles Sutton
• Statistical Deobfuscation of Android Applications Benjamin Bichsel, Veselin Raychev, Petar Tsankov, Martin Vechev
• Recovering Clear, Natural Identifiers from Obfuscated JS Names Bogdan Vasilescu, Casey Casalnuovo, Premkumar Devanbu
• Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts Rohan Bavishi, Michael Pradel, Koushik Sen
• Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
• A General Path-Based Representation for Predicting Program Properties Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav
• code2vec: Learning Distributed Representations of Code Uri Alon, Omer Levy, Eran Yahav
• A Neural Model for Method Name Generation from Functional Description Sa Gao, Chunyang Chen, Zhenchang Xing, Yukun Ma, Wen Song, Shang-Wei Lin
• Recovering Variable Names for Minified Code with Usage Contexts Hieu Tran, Ngoc Tran, Son Nguyen, Hoan Nguyen, Tien N. Nguyen
• Mercem: Method Name Recommendation Based on Call Graph Embedding Hiroshi Yonai, Yasuhiro Hayase, Hiroyuki Kitagawa
• Learning to Sport and Refactor Inconsistent Method Names Kui Liu, Dongsun Kim, Tegawendé F. Bissyandé, Taeyoung Kim, Kisub Kim, Anil Koyuncu, Suntae Kim, Yves Le Traon
• code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
• Method name suggestion with hierarchical attention networks Sihan Xu, Sen Zhang, Weijing Wang, Xinya Cao, Chenkai Guo, Jing Xu.
• A Neural Approach to Decompiled Identifier Renaming Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
• Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav
• Suggesting Natural Method Names to Check Name Consistencies Son Nguyen, Hung Phan, Trinh Le, Tien N. Nguyen
• Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
• Embedding Java Classes with code2vec: Improvements from Variable Obfuscation Rhys Compton, Eibe Frank, Panos Patros, Abigail Koay
• Semantic Robustness of Models of Source Code Jordan Henkel, Goutham Ramakrishnan, Zi Wang, Aws Albarghouthi, Somesh Jha, Thomas Reps
• InCoder: A Generative Model for Code Infilling and Synthesis Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis

🏷 natural language generation

• Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev

🏷 natural language processing

• Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
• Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev

🏷 notebook

• Natural Language to Code Generation in Interactive Data Science Notebooks Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton

🏷 optimization

• End-to-end Deep Learning of Optimization Heuristics Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather
• Synthesizing benchmarks for predictive modeling Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather
• Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
• Neural-Network Guided Expression Transformation Romain Edelmann, Viktor Kunčak
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
• Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
• Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Giuseppe Tagliavini, Andrea Acquaviva
• Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva
• Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
• DeepPERF: A Deep Learning-Based Approach For Improving Software Performance Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu
• Supersonic: Learning to Generate Source Code Optimizations in C/C++ Zimin Chen, Sen Fang, Martin Monperrus
• Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao

🏷 pattern mining

• Mining Idioms from Source Code Miltiadis Allamanis, Charles Sutton
• KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts Dana Movshovitz-Attias, William W. Cohen
• Parameter-Free Probabilistic API Mining across GitHub Jaroslav Fowkes, Charles Sutton
• Mining Semantic Loop Idioms from Big Code Miltiadis Allamanis, Earl T. Barr, Christian Bird, Mark Marron, Charles Sutton
• Topic modeling of public repositories at scale using names in source code Vadim Markovtsev, Eiso Kant
• Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change Patterns Hoan Anh Nguyen, Tien N. Nguyen, Danny Dig, Son Nguyen, Hieu Tran, and Michael Hilton
• Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
• Mining Idioms in the Wild Aishwarya Sivaraman, Rui Abreu, Andrew Scott, Tobi Akomolede, Satish Chandra

🏷 pretraining

• Deep Transfer Learning for Source Code Modeling Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang
• GraphCodeBERT: Pre-training Code Representations with Data Flow Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Jian Yin, Daxin Jiang, Ming Zhou
• PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
• IntelliCode Compose: Code Generation Using Transformer Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan
• CodeBERT: A Pre-Trained Model for Programming and Natural Languages Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
• Pre-trained Contextual Embedding of Source Code Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi
• Contrastive Code Representation Learning Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica
• SCELMo: Source Code Embeddings from Language Models Rafael-Michael Karampatsis, Charles Sutton
• Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
• SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation Xin Wang, Yasheng Wang, Fei Mi, Pingyi Zhou, Yao Wan, Xiao Liu, Li Li, Hao Wu, Jin Liu, Xin Jiang
• DOBF: A Deobfuscation Pre-Training Objective for Programming Languages Baptiste Roziere, Marie-Anne Lachaux, Marc Szafraniec, Guillaume Lample
• Unified Pre-training for Program Understanding and Generation Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
• What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin

🏷 program analysis

• A Factor Graph Model for Software Bug Finding Ted Kremenek, Andrew Y. Ng, Dawson R. Engler.
• Predicting Program Properties from “Big Code” Veselin Raychev, Martin Vechev, Andreas Krause
• Learning a Strategy for Adapting a Program Analysis via Bayesian Optimisation Hakjoo Oh, Hongseok Yang, Kwangkeun Yi.
• A User-Guided Approach to Program Analysis Ravi Mangal, Xin Zhang, Aditya V. Nori, Mayur Naik
• Gated Graph Sequence Neural Networks Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
• Deep Learning to Find Bugs Michael Pradel, Koushik Sen
• Finding Likely Errors with Bayesian Specifications Vijayaraghavan Murali, Swarat Chaudhuri, Chris Jermaine
• User-guided program reasoning using Bayesian inference Mukund Raghothaman, Sulekha Kulkarni, Kihong Heo, Mayur Naik
• Path-Based Function Embedding and its Application to Specification Mining Daniel DeFreez, Aditya V. Thakur, Cindy Rubio-González
• Automated Vulnerability Detection in Source Code Using Deep Representation Learning Rebecca L. Russell, Louis Kim, Lei H. Hamilton, Tomo Lazovich, Jacob A. Harer, Onur Ozdemir, Paul M. Ellingwood, Marc W. McConley
• Learning Loop Invariants for Program Verification Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song
• Neural-Augumented Static Analysis of Android Communication Jinman Zhao, Aws Albarghouthi, Vaibhav Rastogi, Somesh Jha, Damien Octeau
• RefiNym: Using Names to Refine Types Santanu Dash, Miltiadis Allamanis, Earl T. Barr
• Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
• On the Feasibility of Transfer-learning Code Smells using Deep Learning Tushar Sharma, Vasiliki Efstathiou, Panos Louridas, Diomidis Spinellis
• Unsupervised Learning of API Aliasing Specifications Jan Eberhardt, Samuel Steffen, Veselin Raychev, Martin Vechev
• Neural Bug Finding: A Study of Opportunities and Challenges Andrew Habib, Michael Pradel
• Scalable Taint Specification Inference with Big Code V. Chibotaru, B. Bichsel, Veselin Raychev, Martin Vechev
• Neural Program Repair by Jointly Learning to Localize and Repair Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh
• Inferring Javascript types using Graph Neural Networks Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
• Neural Software Analysis Michael Pradel, Satish Chandra
• Learning Graph Structure With A Finite-State Automaton Layer Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
• Predicting Vulnerability in Large Codebases With Deep Code Representation Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi
• SinkFinder: harvesting hundreds of unknown interesting function pairs with just one seed Pan Bian, Bin Liang, Jianjun Huang, Wenchang Shi, Xidong Wang, Jian Zhang
• Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
• Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Giuseppe Tagliavini, Andrea Acquaviva
• Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
• What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
• (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
• A Learning-Based Approach to Static Program Slicing Aashish Yadavally, Yi Li, Shaohua Wang, Tien N. Nguyen
• Predictive Program Slicing via Execution Knowledge-Guided Dynamic Dependence Learning Aashish Yadavally, Yi Li, Tien N. Nguyen

🏷 program synthesis

• Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets V. Lomshakov, S. Kovalchuk, M. Omelchenko, S. Nikolenko, A. Aliev

🏷 question answering

• Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets V. Lomshakov, S. Kovalchuk, M. Omelchenko, S. Nikolenko, A. Aliev

🏷 refactoring

• Mercem: Method Name Recommendation Based on Call Graph Embedding Hiroshi Yonai, Yasuhiro Hayase, Hiroyuki Kitagawa
• Testing Neural Program Analyzers Md Rafiqul Islam Rabin, Ke Wang, Mohammad Amin Alipour
• On the Impact of Refactoring Operations on Code Naturalness Bin Lin, Csaba Nagy, Gabriele Bavota, Michele Lanza
• Recommendation of Move Method Refactoring Using Path-Based Representation of Code Zarina Kurbatova, Ivan Veselov, Yaroslav Golubev, Timofey Bryksin
• Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour
• On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
• Mining Idioms in the Wild Aishwarya Sivaraman, Rui Abreu, Andrew Scott, Tobi Akomolede, Satish Chandra
• Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
• Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn

🏷 repair

• Syntax Errors Just Aren’t Natural: Improving Error Reporting with Language Models Joshua Charles Campbell, Abram Hindle, José Nelson Amaral
• Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
• OverCode: visualizing variation in student solutions to programming problems at scale Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller
• sk_p: a neural program corrector for MOOCs Yewen Pu, Karthik Narasimhan, Armando Solar-Lezama, Regina Barzilay
• Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks Sahil Bhatia, Rishabh Singh
• Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities Martin White, Michele Tufano, Matias Martinez, Martin Monperrus, Denys Poshyvanyk
• DeepFix: Fixing Common C Language Errors by Deep Learning Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade
• Semantic Code Repair using Neuro-Symbolic Transformation Networks Jacob Devlin, Jonathan Uesato, Rishabh Singh, Pushmeet Kohli
• Learning How to Mutate Source Code from Bug-Fixes Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Deep Reinforcement Learning for Programming Language Correction Rahul Gupta, Aditya Kanade, Shirish Shevade
• Learning to Repair Software Vulnerabilities with Generative Adversarial Networks Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin
• Neuro-symbolic program corrector for introductory programming assignments Sahil Bhatia, Pushmeet Kohli, Rishabh Singh
• Learning to Generate Corrective Patches using Neural Machine Translation Hideaki Hata, Emad Shihab, Graham Neubig
• CODIT: Code Editing with Tree-Based Neural Machine Translation Saikat Chakraborty, Miltiadis Allamanis, Baishakhi Ray
• Syntax and Sensibility: Using language models to detect and correct syntax errors Eddie Antonio Santos, Joshua Charles Campbell, Dhvani Patel, Abram Hindle, José Nelson Amaral
• SampleFix: Learning to Correct Programs by Sampling Diverse Fixes Hossein Hajipour, Apratim Bhattacharyya, Cristian-Alexandru Staicu, Mario Fritz
• On Learning Meaningful Code Changes via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Learning to Fix Build Errors with Graph2Diff Neural Networks Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
• SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus
• DeepDelta: Learning to Repair Compilation Errors Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.
• Neural Program Repair by Jointly Learning to Localize and Repair Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh
• Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair Haoye Tian, Kui Liu, Abdoul Kader Kaboreé, Anil Koyuncu, Li Li, Jacques Klein, Tegawendé F. Bissyandé
• Hoppity: Learning Bug Detection and Repair Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
• DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
• Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
• Learning to Find Naming Issues with Big Code and Small Supervision Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin Vechev
• A Semantic Bug Seeding: A Learning-Based Approach for Creating Realistic Bugs Jibesh Patra, Michael Pradel
• Neural Program Repair with Execution-based Backpropagation He Ye, Matias Martinez, Monperrus Martin
• Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing Haiwen Hong, Jingfeng Zhang, Yin Zhang, Yao Wan, Yulei Sui
• DeepMerge: Learning to Merge Programs Elizabeth Dinella, Todd Mytkowicz, Alexey Svyatkovskiy, Christian Bird, Mayur Naik, Shuvendu K. Lahiri
• Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
• DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons Dawn Drain, Colin B. Clement, Guillermo Serrato, Neel Sundaresan
• Generating Bug-Fixes Using Pretrained Transformers Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan
• TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev
• PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair Zimin Chen, Vincent J Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra
• SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics He Ye, Matias Martinez, Xiapu Luo, Tao Zhang, Martin Monperrus
• Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes Cedric Richter, Heike Wehrheim
• Using Developer Discussions to Guide Fixing Bugs in Software Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney
• Demystifying GPT Self-Repair for Code Generation Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, Armando Solar-Lezama
• TraceFixer: Execution Trace-Driven Program Repair Islem Bouzenia, Yangruibo Ding, Kexin Pei, Baishakhi Ray, Michael Pradel
• Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models Iman Saberi, Fateme H. Fard
• SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili, Moshi Wei, Alvine Boaye Belle, Hung Viet Pham, Song Wang
• RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair André Silva, Sen Fang, Martin Monperrus
• DebugBench: Evaluating Debugging Capability of Large Language Models Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Zhiyuan Liu, Maosong Sun
• T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble Reza Gharibi, Mohammad Hadi Sadreddini, Seyed Mostafa Fakhrahmad
• RepairAgent: An Autonomous, LLM-Based Agent for Program Repair Islem Bouzenia, Premkumar Devanbu, Michael Pradel
• DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models Berkay Berabi, Alexey Gronskiy, Veselin Raychev, Gishor Sivanrupan, Victor Chibotaru, Martin Vechev

🏷 representation

• Building Program Vector Representations for Deep Learning Hao Peng, Lili Mou, Ge Li, Yuxuan Liu, Lu Zhang, Zhi Jin.
• Learning to Execute Wojciech Zaremba, Ilya Sutskever
• Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
• Graph-based Statistical Language Model for Code Anh Tuan Nguyen, Tien N. Nguyen
• Toward Deep Learning Software Repositories Martin White, Christopher Vendome, Mario Linares-Vasquez, Denys Poshyvanyk
• Learning to Generate Pseudo-code from Source Code using Statistical Machine Translation Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura
• Exploring the Use of Deep Learning for Feature Location Christopher S. Corley, Kostadin Damevski, Nicholas A. Kraft
• Learning API Usages from Bytecode: A Statistical Approach Tam The Nguyen, Hung Viet Pham, Phong Minh Vu, Tung Thanh Nguyen
• Bugram: bug detection with n-gram language models Song Wang, Devin Chollak, Dana Movshovitz-Attias, Lin Tan
• Convolutional Neural Networks over Tree Structures for Programming Language Processing Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin
• Automatically generating features for learning program analysis heuristics Kwonsoo Chae, Hakjoo Oh, Kihong Heo, Hongseok Yang
• Automatically Learning Semantic Features for Defect Prediction Song Wang, Taiyue Liu, Lin Tan
• Semantically enhanced software traceability using deep learning techniques Jin Guo, Jinghui Cheng, Jane Cleland-Huang
• Neural Attribute Machines for Program Generation Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
• Exploring API Embedding for API Usages and Applications Trong Duc Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Tien N. Nguyen
• SmartPaste: Learning to Adapt Source Code Miltiadis Allamanis, Marc Brockschmidt
• Deep Learning Similarities from Different Representations of Source Code Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Path-Based Function Embedding and its Application to Specification Mining Daniel DeFreez, Aditya V. Thakur, Cindy Rubio-González
• Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code Nghi D. Q. Bui, Lingxiao Jiang
• Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks Nghi D. Q. Bui, Lingxiao Jiang, Yijun Yu
• Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
• Neural Code Comprehension: A Learnable Representation of Code Semantics Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
• Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
• Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• Intelligent code reviews using deep learning Anshul Gupta, Neel Sundaresan
• Deep Learning Type Inference V. J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis
• A General Path-Based Representation for Predicting Program Properties Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav
• code2vec: Learning Distributed Representations of Code Uri Alon, Omer Levy, Eran Yahav
• On the Feasibility of Transfer-learning Code Smells using Deep Learning Tushar Sharma, Vasiliki Efstathiou, Panos Louridas, Diomidis Spinellis
• Mercem: Method Name Recommendation Based on Call Graph Embedding Hiroshi Yonai, Yasuhiro Hayase, Hiroyuki Kitagawa
• Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially Yudong Zhang, Wenhao Zheng, Ming Li
• Learning Execution through Neural Code Fusion Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
• Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks Yi Li, Shaohua Wang, Tien N. Nguyen, Son Van Nguyen
• Capturing source code semantics via tree-based convolution over API-enhanced AST Long Chen, Wei Ye, Shikun Zhang
• code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
• Semantic Source Code Models Using Identifier Embeddings Vasiliki Efstathiou, Diomidis Spinellis
• SAR: Learning Cross-Language API Mappings with Little Knowledge N. D. Q. Bui, Y. Yu, L. Jiang
• Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization Steven H. H. Ding, Benjamin C. M. Fung, Philippe Charland
• Mining Likely Analogical APIs across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding Chunyang Chen, Zhenchang Xing, Yang Liu, Kent Ong Long Xiong
• Import2vec - Learning Embeddings for Software Libraries Bart Theeten, Frederik Vandeputte, Tom Van Cutsem
• PathMiner : A Library for Mining of Path-Based Representations of Code Vladimir Kovalenko, Egor Bogomolov, Timofey Bryksin, Alberto Bacchelli.
• TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• A Literature Study of Embeddings on Source Code Zimin Chen, Martin Monperrus
• Learning Scalable and Precise Representation of Program Semantics Ke Wang
• Program Classification Using Gated Graph Attention Neural Network for Online Programming Service Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
• Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta, Aditya Kanade, Shirish Shevade
• A Novel Neural Source Code Representation based on Abstract Syntax Tree Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
• Modular Tree Network for Source Code Representation Learning Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, Zhi Jin
• Searching a Database of Source Codes Using Contextualized Code Search Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
• Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
• Towards Learning Representations of Binary Executable Files for Security Tasks Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• Contrastive Code Representation Learning Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica
• Unsupervised Learning of General-Purpose Embeddings for Code Changes Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev, Timofey Bryksin
• Disentangled Code Representation Learning for Multiple Programming Languages Jingfeng Zhang, Haiwen Hong, Yin Zhang, Yao Wan, Ye Liu, Yulei Sui
• IdBench: Evaluating Semantic Representations of Identifier Names in Source Code Yaza Wainakh, Moiz Rauf, Michael Pradel
• Multimodal Representation for Neural Code Search Jian Gu, Zimin Chen, Martin Monperrus
• Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
• MulCode: A Multi-task Learning Approach for Source Code Understanding Deze Wang, Yue Yu, Shanshan Li, Wei Dong, Ji Wang, Liao Qing
• Language-Agnostic Representation Learning of Source Code from Structure and Context Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann
• InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• Learning Program Semantics with Code Representations: An Empirical Study Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu
• Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, Xiangke Liao
• SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, Bin Luo
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
• CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik
• Topical: Learning Repository Embeddings from Source Code using Attention Agathe Lherondelle, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran
• Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao

🏷 retrieval

• Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
• RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen

🏷 Reverse Engineering

• Learning to Reverse DNNs from AI Programs Automatically Simin Chen, Hamed Khanpour, Cong Liu, Wei Yang

🏷 review

• Will they like this? Evaluating Code Contributions With Language Models Vincent J. Hellendoorn, Premkumar Devanbu, Alberto Bacchelli
• Intelligent code reviews using deep learning Anshul Gupta, Neel Sundaresan
• CORE: Automating Review Recommendation for Code Changes JingKai Siow, Cuiyun Gao, Lingling Fan, Sen Chen, Yang Liu
• Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva
• CodeReviewer: Pre-Training for Automating Code Review Activities Zhiyu Li, Shuai Lu, Daya Guo, Nan Duan, Shailesh Jannu, Grant Jenks, Deep Majumder, Jared Green, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan
• What is it like to program with artificial intelligence? Advait Sarkar, Andrew D. Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, Ben Zorn

🏷 search

• A Bimodal Modelling of Source Code and Natural Language Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
• Aroma: code recommendation via structural code search Sifei Luan, Di Yang, Celeste Barnaby, Koushik Sen, Satish Chandra
• Deep API Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim.
• A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
• Deep Code Search Xiaodong Gu, Hongyu Zhang, Sunghun Kim.
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt
• Multi-Modal Attention Network Learning for Semantic Source Code Retrieval Yao Wan, Jingdong Shu, Yulei Sui, Guandong Xu, Zhou Zhao, Jian Wu, Philip S. Yu
• CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning Ziyu Yao, Jayavardhan Reddy Peddamail, Huan Sun
• Neural Code Search Evaluation Dataset Hongyu Li, Seohyun Kim, Satish Chandra
• When Deep Learning Met Code Search Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, Satish Chandra
• Neural query expansion for code search Jason Liu, Seohyun Kim, Vijayaraghavan Murali, Swarat Chaudhuri, Satish Chandra
• Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent Geert Heyman, Tom Van Cutsem
• CoNCRA: A Convolutional Neural Network Code Retrieval Approach Marcelo de Rezende Martins, Marco Aurélio Gerosa
• PSCS: A Path-based Neural Model for Semantic Code Search Zhensu Sun, Yan Liu, Chen Yang, Yu Qian
• TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search Wenhua Wang, Yuqun Zhang, Zhengran Zeng, Guandong Xu
• Searching a Database of Source Codes Using Contextualized Code Search Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
• Improving Code Search with Co-Attentive Representation Learning Jianhang Shuai, Ling Xu, Chao Liu, Meng Yan, Xin Xia, Yan Lei
• A Multi-Perspective Architecture for Semantic Code Search Rajarshi Haldar, Lingfei Wu, Jinjun Xiong, Julia Hockenmaier
• Adaptive Deep Code Search Chunyang Ling, Zeqi Lin, Yanzhen Zou, Bing Xie
• Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries Shuhan Yan, Hang Yu, Yuting Chen, Beijun Shen, Lingxiao Jiang
• Deep Graph Matching and Searching for Semantic Code Retrieval Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
• NaturalCC: A Toolkit to Naturalize the Source Code Corpus Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu
• Learning Code-Query Interaction for Enhancing Code Searches Wei Li, Haozhe Qin, Shuhan Yan, Beijun Shen, Yuting Chen
• Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, Shikun Zhang
• OCoR: An Overlapping-Aware Code Retriever Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, Lu Zhang
• CoSQA: 20,000+ Web Queries for Code Search and Question Answering Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan
• DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum
• Multimodal Representation for Neural Code Search Jian Gu, Zimin Chen, Martin Monperrus
• Distilling Transformers for Neural Cross-Domain Search Colin B. Clement, Chen Wu, Dawn Drain, Neel Sundaresan
• Leveraging Language to Learn Program Abstractions and Search Heuristics Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum, Jacob Andreas
• Bag-of-Words Baselines for Semantic Code Search Xinyu Zhang, Ji Xin, Andrew Yates, Jimmy Lin
• Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• Exploring Representation-Level Augmentation for Code Search Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang
• Senatus - A Fast and Accurate Code-to-Code Recommendation Engine Fran Silavong, Sean Moran, Antonios Georgiadis, Rohan Saphal, Robert Otter
• DocCoder: Generating Code by Retrieving and Reading Docs Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao JIang, Graham Neubig
• CodeDSI: Differentiable Code Search Usama Nadeem, Noah Ziems, Shaoen Wu
• Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
• Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search Haochen Li, Xin Zhou, Zhiqi Shen

🏷 static

• A Learning-Based Approach to Static Program Slicing Aashish Yadavally, Yi Li, Shaohua Wang, Tien N. Nguyen

🏷 static analysis

• Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools Ugur Koc, Parsa Saadatpanah, Jeffrey S. Foster, Adam A. Porter.
• Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
• Predicting Vulnerability in Large Codebases With Deep Code Representation Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi
• Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu
• Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
• Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
• Learning to Answer Semantic Queries over Code Surya Prakash Sahu, Madhurima Mandal, Shikhar Bharadwaj, Aditya Kanade, Petros Maniatis, Shirish Shevade
• Learning to Reduce False Positives in Analytic Bug Detectors Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
• The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models Haonan Li, Yu Hao, Yizhuo Zhai, Zhiyun Qian
• A Static Evaluation of Code Completion by Large Language Models Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
• (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
• Beware of the Unexpected: Bimodal Taint Analysis Yiu Wai Chow, Max Schäfer, Michael Pradel

🏷 style

• Learning Natural Coding Conventions Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
• STYLE-ANALYZER: fixing code style inconsistencies with interpretable unsupervised algorithms Vadim Markovtsev, Waren Long, Hugo Mougard, Konstantin Slavnov, Egor Bulychev

🏷 summarization

• Natural Language Models for Predicting Programming Comments Dana Movshovitz-Attias, William W. Cohen
• A Convolutional Attention Network for Extreme Summarization of Source Code Miltiadis Allamanis, Hao Peng, Charles Sutton
• Summarizing Source Code using a Neural Attention Model Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• Autofolding for Source Code Summarization Jaroslav Fowkes, Razan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton
• Abridging Source Code Binhang Yuan, Vijayaraghavan Murali, Christopher Jermaine
• CodeSum: Translate Program Language to Natural Language Xing Hu, Yuhan Wei, Ge Li, Zhi Jin
• A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
• A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes Pablo Loyola, Edison Marrese-Taylor, Yutaka Matsuo
• Content Aware Source Code Change Description Generation Pablo Loyola, Edison Marrese-Taylor, Jorge Balazs, Yutaka Matsuo, Fumiko Satoh
• Improving Automatic Source Code Summarization via Deep Reinforcement Learning Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu
• Neural-Machine-Translation-Based Commit Message Generation: How Far Are We? Zhongxin Liu, Xin Xia, Ahmed E. Hassan, David Lo, Zhenchang Xing, Xinyu Wang
• code2vec: Learning Distributed Representations of Code Uri Alon, Omer Levy, Eran Yahav
• A Neural Model for Method Name Generation from Functional Description Sa Gao, Chunyang Chen, Zhenchang Xing, Yukun Ma, Wen Song, Shang-Wei Lin
• code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
• Automatic Source Code Summarization with Extended Tree-LSTM Yusuke Shido, Yasuaki Kobayashi, Akihiro Yamamoto, Atsushi Miyamoto, Tadayuki Matsumura
• Commit Message Generation for Source Code Changes Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, Jian Lu
• Code Generation as a Dual Task of Code Summarization Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
• Structured Neural Summarization Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
• A Neural Model for Generating Natural Language Summaries of Program Subroutines Alexander LeClair, Siyuan Jiang, Collin McMillan
• Recommendations for Datasets for Source Code Summarization Alexander LeClair, Collin McMillan
• Improved Automatic Summarization of Subroutines via Attention to File Context Sakib Haque, Alexander LeClair, Lingfei Wu, Collin McMillan
• CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs Yanlin Wang, Lun Du, Ensheng Shi, Yuxuan Hu, Shi Han, Dongmei Zhang
• A Transformer-based Approach for Source Code Summarization Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
• Improved Code Summarization via a Graph Neural Network Alexander LeClair, Sakib Haque, Lingfei Wu, Collin McMillan
• NaturalCC: A Toolkit to Naturalize the Source Code Corpus Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu
• Learning to Represent Programs with Heterogeneous Graphs Wenhan Wang, Kechi Zhang, Ge Li, Zhi Jin
• Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, Shikun Zhang
• On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
• Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors Junayed Mahmud, Fahim Faisal, Raihan Islam Arnob, Antonios Anastasopoulos, Kevin Moran
• Learning to Describe Solutions for Bug Reports Based on Developer Discussions Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• Assemble Foundation Models for Automatic Code Summarization Jian Gu, Pasquale Salza, Harald C. Gall
• InCoder: A Generative Model for Code Infilling and Synthesis Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
• Learning code summarization from a small and local dataset Toufique Ahmed, Premkumar Devanbu
• Improving Few-Shot Prompts with Relevant Static Analysis Products Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, Earl T. Barr
• Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models Iman Saberi, Fateme H. Fard

🏷 survey

• Neural Software Analysis Michael Pradel, Satish Chandra
• A Survey on Deep Learning for Software Engineering Yanming Yang, Xin Xia, David Lo, John Grundy
• Deep Learning & Software Engineering: State of Research and Future Directions Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang
• Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors Junayed Mahmud, Fahim Faisal, Raihan Islam Arnob, Antonios Anastasopoulos, Kevin Moran
• A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, Denys Poshyvanyk
• Deep Learning based Vulnerability Detection: Are We There Yet? Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, Baishakhi Ray
• A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks Beatrice Casey, Joanna C. S. Santos, George Perry

🏷 synthesis

• NLyze: Interactive Programming by Natural Language for SpreadSheet Data Analysis and Manipulation Sumit Gulwani, Mark Marron
• Synthesizing Java expressions from free-form queries Tihomir Gvero, Viktor Kuncak
• SPoC: Search-based Pseudocode to Code Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy S. Liang
• AutoPandas: neural-backed generators for program synthesis Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica
• Semantic Scaffolds for Pseudocode-to-Code Generation Ruiqi Zhong, Mitchell Stern, Dan Klein
• Unit Test Case Generation with Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• IntelliCode Compose: Code Generation Using Transformer Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan
• Evaluating Large Language Models Trained on Code Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, Will Guss, Alex Nichol, Igor Babuschkin, Suchir Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba
• DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum
• Program Synthesis with Large Language Models Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton
• A large-scale benchmark for few-shot program induction and synthesis Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, Joshua Tenenbaum
• Leveraging Language to Learn Program Abstractions and Search Heuristics Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum, Jacob Andreas
• Neural Program Generation Modulo Static Analysis Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Chris Jermaine
• A Conversational Paradigm for Program Synthesis Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong
• I Speak, You Verify: Toward Trustworthy Neural Program Synthesis Darren Key, Wen-Ding Li, Kevin Ellis
• CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
• Grounded Copilot: How Programmers Interact with Code-Generating Models Shraddha Barke, Michael B. James, Nadia Polikarpova

🏷 test generation

• Unit Test Case Generation with Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• TOGA: A Neural Method for Test Oracle Generation Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, Shuvendu K. Lahiri
• Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev

🏷 tool

• PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code Egor Spirin, Egor Bogomolov, Vladimir Kovalenko, Timofey Bryksin
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
• (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
• A Learning-Based Approach to Static Program Slicing Aashish Yadavally, Yi Li, Shaohua Wang, Tien N. Nguyen
• Predictive Program Slicing via Execution Knowledge-Guided Dynamic Dependence Learning Aashish Yadavally, Yi Li, Tien N. Nguyen

🏷 topic modeling

• Topic modeling of public repositories at scale using names in source code Vadim Markovtsev, Eiso Kant

🏷 topic modelling

• Topical: Learning Repository Embeddings from Source Code using Attention Agathe Lherondelle, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran

🏷 traceability

• Semantically enhanced software traceability using deep learning techniques Jin Guo, Jinghui Cheng, Jane Cleland-Huang

🏷 Transformer

• Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair Haoye Tian, Kui Liu, Abdoul Kader Kaboreé, Anil Koyuncu, Li Li, Jacques Klein, Tegawendé F. Bissyandé
• Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
• Empirical Study of Transformers for Source Code Nadezhda Chirkova, Sergey Troshin
• Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
• Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• Show Your Work: Scratchpads for Intermediate Computation with Language Models Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback Mike Wu, Noah D. Goodman, Chris Piech, Chelsea Finn
• CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
• CoTexT: Multi-task Learning with Code-Text Transformer Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye
• Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors Junayed Mahmud, Fahim Faisal, Raihan Islam Arnob, Antonios Anastasopoulos, Kevin Moran
• ConTest: A Unit Test Completion Benchmark featuring Context Johannes Villmow, Jonas Depoix, Adrian Ulges
• Jointly Learning to Repair Code and Generate Commit Message Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li
• Co-Training for Commit Classification Jian Yi, David Lee, Hai Leong Chieu
• Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
• Learning Type Annotation: Is Big Data Enough? Kevin Jesse, Premkumar Devanbu, Toufique Ahmed
• Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
• Program Synthesis with Large Language Models Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton
• CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi
• An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri
• Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation Gabriel Orlanski, Alex Gittens
• Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
• How could Neural Networks understand Programs? Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
• Improving Code Autocompletion with Transfer Learning Wen Zhou, Seohyun Kim, Vijayaraghavan Murali, Gareth Ari Aye
• DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons Dawn Drain, Colin B. Clement, Guillermo Serrato, Neel Sundaresan
• Generating Bug-Fixes Using Pretrained Transformers Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan
• Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
• Language-Agnostic Representation Learning of Source Code from Structure and Context Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann
• Distilling Transformers for Neural Cross-Domain Search Colin B. Clement, Chen Wu, Dawn Drain, Neel Sundaresan
• TreeBERT: A Tree-Based Pre-Trained Model for Programming Language Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, Lei Lyu
• Time-Efficient Code Completion Model for the R Programming Language Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev
• What do pre-trained code models know about code? Anjan Karmakar, Romain Robbes
• CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes, Burkhard Rost
• Unified Pre-training for Program Understanding and Generation Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• On Multi-Modal Learning of Editing Source Code Saikat Chakraborty, Baishakhi Ray
• CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu
• DIRECT : A Transformer-based Model for Decompiled Identifier Renaming Vikram Nitin, Anthony Saieva, Baishakhi Ray, Gail Kaiser
• Code Translation with Compiler Representations Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, Gabriel Synnaeve
• Learning to Model Editing Processes Machel Reid, Graham Neubig
• SantaCoder: don’t reach for the stars! Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muenninghoff, Mayank Mishra, Alex Gu, Manan Den, Longesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Terry Yue Zhuo, Francesco De Toni, Bernanrdo Garcia del Rio, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Michael Lappert, Ian Yu, Paulo Villegas, Jia Li, David Lansy, Huu Nguyen, Danish Contractor, Luis Villa, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Arjun Guha, Harm de Vries, Leonadro von Werra
• Learning To Predict User-Defined Types Kevin Jesse, Premkumar T. Devanbu, Anand Sawant
• Efficient Training of Language Models to Fill in the Middle Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen
• CoditT5: Pretraining for Source Code and Natural Language Editing Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Jessy Li, Milos Gligoric
• Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? Jean-Baptiste Döderlein, Mathieu Acher, Djamel Eddine Khelladi, Benoit Combemale
• Exploring Representation-Level Augmentation for Code Search Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang
• A Systematic Evaluation of Large Language Models of Code Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn
• A Conversational Paradigm for Program Synthesis Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong
• Synchromesh: Reliable code generation from pre-trained language models Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani
• TOGA: A Neural Method for Test Oracle Generation Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, Shuvendu K. Lahiri
• Learning to Complete Code with Sketches Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis
• UniXcoder: Unified Cross-Modal Pre-training for Code Representation Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, Jian Yin
• InCoder: A Generative Model for Code Infilling and Synthesis Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis
• Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes Cedric Richter, Heike Wehrheim
• CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
• SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, Bin Luo
• DocCoder: Generating Code by Retrieving and Reading Docs Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao JIang, Graham Neubig
• ReACC: A Retrieval-Augmented Code Completion Framework Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, Alexey Svyatkovskiy
• Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
• Using Developer Discussions to Guide Fixing Bugs in Software Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney
• An Extensive Study on Pre-trained Models for Program Understanding and Generation Zhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li, Yuqun Zhang, Lingming Zhang
• CV4Code: Sourcecode Understanding via Visual Code Representations Ruibo Shi, Lili Tao, Rohan Saphal, Fran Silavong, Sean J. Moran
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
• Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
• Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code Patrick Bareiß, Beatriz Souza, Marcelo d'Amorim, Michael Pradel
• Learning code summarization from a small and local dataset Toufique Ahmed, Premkumar Devanbu
• Learning to Answer Semantic Queries over Code Surya Prakash Sahu, Madhurima Mandal, Shikhar Bharadwaj, Aditya Kanade, Petros Maniatis, Shirish Shevade
• DeepPERF: A Deep Learning-Based Approach For Improving Software Performance Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu
• Using Deep Learning to Generate Complete Log Statements Antonio Mastropaolo, Luca Pascarella, Gabriele Bavota
• Learning to Reduce False Positives in Analytic Bug Detectors Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan
• Exploring and Evaluating Personalized Models for Code Generation Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin Clement, Neel Sundaresan, Michele Tufano
• Repository-Level Prompt Generation for Large Language Models of Code Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
• What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
• Improving Few-Shot Prompts with Relevant Static Analysis Products Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, Earl T. Barr
• CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code Shuyan Zhou, Uri Alon, Sumit Agarwal, Graham Neubig
• StarCoder: may the source be with you! Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
• Large Language Models and Simple, Stupid Bugs Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu, Emily Morgan
• TypeT5: Seq2seq Type Inference using Static Analysis Jiayi Wei, Greg Durrett, Isil Dillig
• TraceFixer: Execution Trace-Driven Program Repair Islem Bouzenia, Yangruibo Ding, Kexin Pei, Baishakhi Ray, Michael Pradel
• Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models Iman Saberi, Fateme H. Fard
• Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
• CodeGen2: Lessons for Training LLMs on Programming and Natural Languages Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, Yingbo Zhou
• RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
• Code Execution with Pre-trained Language Models Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D. Q. Bui, Junnan Li, Steven C. H. Hoi
• CodeScore: Evaluating Code Generation by Learning Code Execution Yihong Dong, Jiazheng Ding, Xue Jiang, Zhuo Li, Ge Li, Zhi Jin
• DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection Yizheng Chen, Zhoujie Ding, Xinyun Chen, David Wagner
• Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation Xin-Ye Li, Jiang-Tian Xue, Zheng Xie, Ming Li
• T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble Reza Gharibi, Mohammad Hadi Sadreddini, Seyed Mostafa Fakhrahmad

🏷 Transformers

• Studying LLM Performance on Closed- and Open-source Data Toufique Ahmed, Christian Bird, Premkumar Devanbu, Saikat Chakraborty
• DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang

🏷 translation

• Automatically Testing Functional Properties of Code Translation Models Hasan Ferit Eniser, Valentin Wüstholz, Maria Christakis
• LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang

🏷 types

• Predicting Program Properties from “Big Code” Veselin Raychev, Martin Vechev, Andreas Krause
• RefiNym: Using Names to Refine Types Santanu Dash, Miltiadis Allamanis, Earl T. Barr
• Deep Learning Type Inference V. J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis
• TypeWriter: Neural Type Prediction with Search-based Validation Michael Pradel, Georgios Gousios, Jason Liu, Satish Chandra.
• NL2Type: Inferring JavaScript Function Types from Natural Language Information Rabee Sohail Malik, Jibesh Patra, Michael Pradel
• Inferring Javascript types using Graph Neural Networks Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
• Learning Lenient Parsing & Typing via Indirect Supervision Toufique Ahmed, Vincent Hellendoorn, Premkumar Devanbu
• OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton
• LambdaNet: Probabilistic Type Inference using Graph Neural Networks Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
• Typilus: Neural Type Hints Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
• Adversarial Robustness for Code Pavol Bielik, Martin Vechev
• Learning Type Annotation: Is Big Data Enough? Kevin Jesse, Premkumar Devanbu, Toufique Ahmed
• ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference Amir M. Mir, Evaldas Latoskinas, Georgios Gousios
• Type4Py: Deep Similarity Learning-Based Type Inference for Python Amir M. Mir, Evaldas Latoskinas, Sebastian Proksch, Georgios Gousios
• Learning To Predict User-Defined Types Kevin Jesse, Premkumar T. Devanbu, Anand Sawant
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
• TypeT5: Seq2seq Type Inference using Static Analysis Jiayi Wei, Greg Durrett, Isil Dillig
• Generative Type Inference for Python Yun Peng, Chaozheng Wang, Wenxuan Wang, Cuiyun Gao, Michael R. Lyu

🏷 variable misuse

• SmartPaste: Learning to Adapt Source Code Miltiadis Allamanis, Marc Brockschmidt
• Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
• Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
• Neural Program Repair by Jointly Learning to Localize and Repair Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh
• Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
• CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik

🏷 verification

• Learning Loop Invariants for Program Verification Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song
• ConTest: A Unit Test Completion Benchmark featuring Context Johannes Villmow, Jonas Depoix, Adrian Ulges

🏷 vulnerability

• DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection Wenbo Wang, Tien N. Nguyen, Shaohua Wang, Yi Li, Jiyuan Zhang, Aashish Yadavally
• DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection Yizheng Chen, Zhoujie Ding, Xinyun Chen, David Wagner
• DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models Berkay Berabi, Alexey Gronskiy, Veselin Raychev, Gishor Sivanrupan, Victor Chibotaru, Martin Vechev
• A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks Beatrice Casey, Joanna C. S. Santos, George Perry