Publications by Tag

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

adversarial API autocomplete benchmark bimodal clone code completion code generation code similarity compilation completion dataset decompilation defect deobfuscation documentation dynamic edit education evaluation execution feature location fuzzing generalizability GNN grammar human evaluation information extraction interpretability language model large language models logging memorization metrics migration naming natural language generation natural language processing optimization pattern mining pretraining program analysis program synthesis question answering refactoring repair representation retrieval review search static analysis style summarization survey synthesis test generation tool topic modeling topic modelling traceability Transformer 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
• Adversarial Robustness for Code Pavol Bielik, Martin Vechev
• 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
• 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
• Deep API Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim.
• Parameter-Free Probabilistic API Mining across GitHub Jaroslav Fowkes, Charles Sutton
• 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
• 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
• 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
• Bayesian Sketch Learning for Program Synthesis Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
• Polyglot Semantic Parsing in APIs Kyle Richardson, Jonathan Berant, Jonas Kuhn
• Unsupervised Learning of API Aliasing Specifications Jan Eberhardt, Samuel Steffen, Veselin Raychev, Martin Vechev
• 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
• SAR: Learning Cross-Language API Mappings with Little Knowledge N. D. Q. Bui, Y. Yu, L. Jiang

🏷 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
• Graph-based Statistical Language Model for Code Anh Tuan Nguyen, Tien N. Nguyen
• Intelligent Code Completion with Bayesian Networks Sebastian Proksch, Johannes Lerch, Mira Mezini
• 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
• Sequence Model Design for Code Completion in the Modern IDE Gareth Ari Aye, Gail E. Kaiser
• Learning Autocompletion from Real-World Datasets Gareth Ari Aye, Seohyun Kim, Hongyu Li
• 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
• Code Prediction by Feeding Trees to Transformers Seohyun Kim, Jinman Zhao, Yuchi Tian, Satish Chandra
• 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
• On the Embeddings of Variables in Recurrent Neural Networks for Source Code Nadezhda Chirkova
• Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
• 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
• Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
• 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

🏷 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
• 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
• Synthesizing Java expressions from free-form queries Tihomir Gvero, Viktor Kuncak
• Latent Predictor Networks for Code Generation Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
• Summarizing Source Code using a Neural Attention Model Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• The Code2Text Challenge: Text Generation in Source Code Libraries Kyle Richardson, Sina Zarrieß, Jonas Kuhn
• pix2code: Generating Code from a Graphical User Interface Screenshot Tony Beltramelli
• Function Assistant: A Tool for NL Querying of APIs Kyle Richardson, Jonas Kuhn
• CodeSum: Translate Program Language to Natural Language Xing Hu, Yuhan Wei, Ge Li, Zhi Jin
• Automatically Generating Commit Messages from Diffs using Neural Machine Translation Siyuan Jiang, Ameer Armaly, Collin McMillan
• Program Synthesis from Natural Language Using Recurrent Neural Networks Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, Michael D. Ernst
• Learning Technical Correspondences in Technical Documentation Kyle Richardson, Jonas Kuhn
• A Syntactic Neural Model for General-Purpose Code Generation Pengcheng Yin, Graham Neubig
• 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
• 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
• A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
• 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
• TypeWriter: Neural Type Prediction with Search-based Validation Michael Pradel, Georgios Gousios, Jason Liu, Satish Chandra.
• SPoC: Search-based Pseudocode to Code Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy S. Liang
• 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
• Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially Yudong Zhang, Wenhao Zheng, Ming Li
• 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
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• 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
• 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
• Learning to Update Natural Language Comments Based on Code Changes Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• TAG : Type Auxiliary Guiding for Code Comment Generation Ruichu Cai, Zhihao Liang, Boyan Xu, Zijian Li, Yuexing Hao, Yao Chen
• Co-Training for Commit Classification Jian Yi, David Lee, Hai Leong Chieu

🏷 clone

• Deep Learning Code Fragments for Code Clone Detection Martin White, Michele Tufano, Christopher Vendome, Denys Poshyvanyk.
• Oreo: detection of clones in the twilight zone Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Pierre Baldi, Cristina Lopes
• Deep Learning Similarities from Different Representations of Source Code Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• 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
• Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree Wenhan Wang, Ge Li, Bo Ma, Xin Xia, Zhi Jin
• Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
• funcGNN: A Graph Neural Network Approach to Program Similarity Aravind Nair, Avijit Roy, Karl Meinke
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
• 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

🏷 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
• NLyze: Interactive Programming by Natural Language for SpreadSheet Data Analysis and Manipulation Sumit Gulwani, Mark Marron
• Code Completion with Statistical Language Models Veselin Raychev, Martin Vechev, Eran Yahav
• Visualizing and Understanding Recurrent Networks Andrej Karpathy, Justin Johnson, Li Fei-Fei
• Synthesizing Java expressions from free-form queries Tihomir Gvero, Viktor Kuncak
• Latent Predictor Networks for Code Generation Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
• Learning Programs from Noisy Data Veselin Raychev, Pavol lBielik, Martin Vechev, Andreas Krause
• A deep language model for software code Hoa Khanh Dam, Truyen Tran, Trang Pham
• PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
• Synthesizing benchmarks for predictive modeling Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather
• pix2code: Generating Code from a Graphical User Interface Screenshot Tony Beltramelli
• DeepFix: Fixing Common C Language Errors by Deep Learning Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade
• 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
• 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
• Mapping Language to Code in Programmatic Context Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• Bayesian Sketch Learning for Program Synthesis Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
• 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
• 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
• A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
• 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
• SampleFix: Learning to Correct Programs by Sampling Diverse Fixes Hossein Hajipour, Apratim Bhattacharyya, Cristian-Alexandru Staicu, Mario Fritz
• Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
• SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus
• DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu
• Code Generation as a Dual Task of Code Summarization Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
• A case study on machine learning for synthesizing benchmarks Andrés Goens, Alexander Brauckmann, Sebastian Ertel, Chris Cummins, Hugh Leather, Jeronimo Castrillon
• Structural Language Models for Any-Code Generation Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav
• Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
• A Grammar-Based Structural CNN Decoder for Code Generation Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang
• Incorporating External Knowledge through Pre-training for Natural Language to Code Generation Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, Graham Neubig
• Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• Unit Test Case Generation with 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
• Semantic Scaffolds for Pseudocode-to-Code Generation Ruiqi Zhong, Mitchell Stern, Dan Klein
• Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• 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
• Energy-Based Models for Code Generation under Compilability Constraints Tomasz Korbak, Hady Elsahar, Marc Dymetman, Germán Kruszewski
• Shellcode_IA32: A Dataset for Automatic Shellcode Generation Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella, Bojan Cukic, Samira Shaikh
• Open-ended Knowledge Tracing Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew Lan
• 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
• 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
• CV4Code: Sourcecode Understanding via Visual Code Representations Ruibo Shi, Lili Tao, Rohan Saphal, Fran Silavong, Sean J. Moran
• 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

🏷 compilation

• A Neural Approach to Decompiled Identifier Renaming Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
• DeepDelta: Learning to Repair Compilation Errors Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Static Neural Compiler Optimization via Deep Reinforcement Learning Rahim Mammadli, Ali Jannesari, Felix Wolf
• 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

🏷 dataset

• A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
• Public Git Archive: a Big Code dataset for all Vadim Markovtsev, Waren Long
• Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow Pengcheng Yin, B. Deng, E. Chen, B. Vasilescu, Graham Neubig
• StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow Ziyu Yao, Daniel S. Weld, Wei-Peng Chen, Huan Sun
• Neural Code Search Evaluation Dataset Hongyu Li, Seohyun Kim, Satish Chandra
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt
• The Adverse Effects of Code Duplication in Machine Learning Models of Code Miltiadis Allamanis
• JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer
• Recommendations for Datasets for Source Code Summarization Alexander LeClair, Collin McMillan
• Code and Named Entity Recognition in StackOverflow Jeniya Tabassum, Mounica Maddela, Wei Xu, Alan Ritter
• 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
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather
• Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data Moshe Hazoom, Vibhor Malik, Ben Bogin
• 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
• 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
• CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
• Impact of Evaluation Methodologies on Code Summarization Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, Milos Gligoric
• Shellcode_IA32: A Dataset for Automatic Shellcode Generation Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella, Bojan Cukic, Samira Shaikh
• 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
• JEMMA: An Extensible Java Dataset for ML4Code Applications Anjan Karmakar, Miltiadis Allamanis, Romain Robbes
• Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
• DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection Yizheng Chen, Zhoujie Ding, Xinyun Chen, David Wagner
• Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani

🏷 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

🏷 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
• Automatically Learning Semantic Features for Defect Prediction Song Wang, Taiyue Liu, Lin Tan
• Bugram: bug detection with n-gram language models Song Wang, Devin Chollak, Dana Movshovitz-Attias, 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
• Exploring the Naturalness of Buggy Code with Recurrent Neural Network Jack Lanchantin, Ji Gao
• 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
• 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
• Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
• OffSide: Learning to Identify Mistakes in Boundary Conditions Jón Arnar Briem, Jordi Smit, Hendrig Sellik, Pavel Rapoport, Georgios Gousios, Maurício Aniche.
• 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
• Improving Automatic Source Code Summarization via Deep Reinforcement Learning Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu
• Deep Learning to Detect Redundant Method Comments Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
• 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
• TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search Wenhua Wang, Yuqun Zhang, Zhengran Zeng, Guandong Xu
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
• Automating Just-In-Time Comment Updating Zhongxin Liu, Xin Xia, Meng Yan, Shanping Li
• Code to Comment "Translation": Data, Metrics, Baselining & Evaluation David Gros, Hariharan Sezhiyan, Premkumar Devanbu, Zhou Yu
• 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
• 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
• Learning to Update Natural Language Comments Based on Code Changes Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• TAG : Type Auxiliary Guiding for Code Comment Generation Ruichu Cai, Zhihao Liang, Boyan Xu, Zijian Li, Yuexing Hao, Yao Chen
• 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
• Blended, precise semantic program embeddings Ke Wang, Zhendong Su
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
• TraceFixer: Execution Trace-Driven Program Repair Islem Bouzenia, Yangruibo Ding, Kexin Pei, Baishakhi Ray, Michael Pradel

🏷 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
• A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes Pablo Loyola, Edison Marrese-Taylor, Yutaka Matsuo
• Automatically Generating Commit Messages from Diffs using Neural Machine Translation Siyuan Jiang, Ameer Armaly, Collin McMillan
• Neural-Machine-Translation-Based Commit Message Generation: How Far Are We? Zhongxin Liu, Xin Xia, Ahmed E. Hassan, David Lo, Zhenchang Xing, Xinyu Wang
• 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
• 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 to Represent Edits Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
• Generating commit messages from diffs using pointer-generator network Qin Liu, Zihe Liu, Hongming Zhu, Hongfei Fan, Bowen Du, Yu Qian.
• Learning to Fix Build Errors with Graph2Diff Neural Networks Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
• DeepDelta: Learning to Repair Compilation Errors Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.
• 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
• On Learning Meaningful Code Changes via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Commit Message Generation for Source Code Changes Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, Jian Lu
• DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
• Copy that! Editing Sequences by Copying Spans Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt
• 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
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
• Hoppity: Learning Bug Detection and Repair Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang
• CC2Vec: Distributed Representations of Code Changes Thong Hoang, Hong Jin Kang, Julia Lawall, David Lo
• Learning to Update Natural Language Comments Based on Code Changes Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
• 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

🏷 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
• An Extensive Study on Pre-trained Models for Program Understanding and Generation Zhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li, Yuqun Zhang, Lingming Zhang
• CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code Aryaz Eghbali, Michael Pradel
• Productivity Assessment of Neural Code Completion Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew Rice, Devon Rifkin, Edward Aftandilian
• 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
• Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
• CodeScore: Evaluating Code Generation by Learning Code Execution Yihong Dong, Jiazheng Ding, Xue Jiang, Zhuo Li, Ge Li, Zhi Jin
• 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

🏷 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
• 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
• DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu
• NEUZZ: Efficient Fuzzing with Neural Program Smoothing Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana
• 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

🏷 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

🏷 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
• Program Classification Using Gated Graph Attention Neural Network for Online Programming Service Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
• Inferring Javascript types using Graph Neural Networks Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
• Simulating Execution Time of Tensor Programs using Graph Neural Networks Jakub M. Tomczak, Romain Lepert, Auke Wiggers
• 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
• Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav
• AutoPandas: neural-backed generators for program synthesis Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica
• Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
• Learning to Fuzz from Symbolic Execution with Application to Smart Contracts Jingxuan He, Mislav Balunović, Nodar Ambroladze, Petar Tsankov, Martin Vechev
• LambdaNet: Probabilistic Type Inference using Graph Neural Networks Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
• 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
• Towards Learning Representations of Binary Executable Files for Security Tasks Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan
• Learning Semantic Program Embeddings with Graph Interval Neural Network Yu Wang, Fengjuan Gao, Linzhang Wang, Ke Wang
• Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
• Typilus: Neural Type Hints Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
• Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
• 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
• Learning Graph Structure With A Finite-State Automaton Layer Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
• Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu
• funcGNN: A Graph Neural Network Approach to Program Similarity Aravind Nair, Avijit Roy, Karl Meinke
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt

🏷 grammar

• Structured Statistical Syntax Tree Prediction Cyrus Omar
• Building Program Vector Representations for Deep Learning Hao Peng, Lili Mou, Ge Li, Yuxuan Liu, Lu Zhang, Zhi Jin.
• Mining Idioms from Source Code Miltiadis Allamanis, Charles Sutton
• Structured Generative Models of Natural Source Code Chris J. Maddison, Daniel Tarlow
• 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
• Convolutional Neural Networks over Tree Structures for Programming Language Processing Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin
• Learning Programs from Noisy Data Veselin Raychev, Pavol lBielik, Martin Vechev, Andreas Krause
• PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
• Mining Semantic Loop Idioms from Big Code Miltiadis Allamanis, Earl T. Barr, Christian Bird, Mark Marron, Charles Sutton
• Neural Attribute Machines for Program Generation Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
• 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
• Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
• A Novel Neural Source Code Representation based on Abstract Syntax Tree Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu
• Neural-Network Guided Expression Transformation Romain Edelmann, Viktor Kunčak
• Learning-based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection Lutz Büch, Artur Andrzejak
• Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
• Capturing source code semantics via tree-based convolution over API-enhanced AST Long Chen, Wei Ye, Shikun Zhang
• A Grammar-Based Structural CNN Decoder for Code Generation Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang
• Automatic Source Code Summarization with Extended Tree-LSTM Yusuke Shido, Yasuaki Kobayashi, Akihiro Yamamoto, Atsushi Miyamoto, Tadayuki Matsumura
• PathMiner : A Library for Mining of Path-Based Representations of Code Vladimir Kovalenko, Egor Bogomolov, Timofey Bryksin, Alberto Bacchelli.
• DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
• 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
• PSCS: A Path-based Neural Model for Semantic Code Search Zhensu Sun, Yan Liu, Chen Yang, Yu Qian
• 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

• Productivity Assessment of Neural Code Completion Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew Rice, Devon Rifkin, Edward Aftandilian
• What is it like to program with artificial intelligence? Advait Sarkar, Andrew D. Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, Ben Zorn
• 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
• Grounded Copilot: How Programmers Interact with Code-Generating Models Shraddha Barke, Michael B. James, Nadia Polikarpova
• Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models Priyan Vaithilingam, Tianyi Zhang, Elena Glassman

🏷 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
• A Deep Learning Approach to Identifying Source Code in Images and Video Jordan Ott, Abigail Atchison, Paul Harnack, Adrienne Bergh, Erik Linstead.
• Evaluation of Type Inference with Textual Cues Amirreza A. Shirani, A. Pastor Lopez-Monroy, Fabio Gonzalez, Thamar Solorio, Mohammad Amin Alipour
• 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

🏷 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
• A Statistical Semantic Language Model for Source Code Tung Thanh Nguyen, Anh Tuan Nguyen, Hoan Anh Nguyen, Tien N. Nguyen
• Mining Source Code Repositories at Massive Scale Using Language Modeling Miltiadis Allamanis, Charles Sutton
• Structured Statistical Syntax Tree Prediction Cyrus Omar
• 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
• Syntax Errors Just Aren’t Natural: Improving Error Reporting with Language Models Joshua Charles Campbell, Abram Hindle, José Nelson Amaral
• Code Completion with Statistical Language Models Veselin Raychev, Martin Vechev, Eran Yahav
• On the Localness of Software Zhaopeng Tu, Zhendong Su, Premkumar Devanbu
• 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
• Will they like this? Evaluating Code Contributions With Language Models Vincent J. Hellendoorn, Premkumar Devanbu, Alberto Bacchelli
• Products, Developers, and Milestones: How Should I Build My N-Gram Language Model Juliana Saraiva, Christian Bird, Thomas Zimmermann
• 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
• PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
• A Language Model for Statements of Software Code Yixiao Yang, Yu Jiang, Ming Gu, Jiaguang Sun, Jian Gao, Han Liu
• Code Completion with Neural Attention and Pointer Networks Jian Li, Yue Wang, Michael R. Lyu, Irwin King
• Are Deep Neural Networks the Best Choice for Modeling Source Code? Vincent J. Hellendoorn, Premkumar Devanbu
• 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
• 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
• 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
• Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer Suyoung Lee, HyungSeok Han, Sang Kil Cha, Sooel Son
• 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
• Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes Charles Sutton, Andrea Janes
• 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
• 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
• CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
• 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
• Efficient Training of Language Models to Fill in the Middle Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen
• Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
• Assemble Foundation Models for Automatic Code Summarization Jian Gu, Pasquale Salza, Harald C. Gall
• Synchromesh: Reliable code generation from pre-trained language models Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani
• A Systematic Evaluation of Large Language Models of Code Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn
• 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
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
• 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

🏷 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

🏷 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
• Divide-and-Conquer Approach for Multi-phase Statistical Migration for Source Code 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
• 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
• Statistical Deobfuscation of Android Applications Benjamin Bichsel, Veselin Raychev, Petar Tsankov, Martin Vechev
• A Convolutional Attention Network for Extreme Summarization of Source Code Miltiadis Allamanis, Hao Peng, Charles Sutton
• 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
• Mercem: Method Name Recommendation Based on Call Graph Embedding Hiroshi Yonai, Yasuhiro Hayase, Hiroyuki Kitagawa
• 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
• A Neural Model for Method Name Generation from Functional Description Sa Gao, Chunyang Chen, Zhenchang Xing, Yukun Ma, Wen Song, Shang-Wei Lin
• Method name suggestion with hierarchical attention networks Sihan Xu, Sen Zhang, Weijing Wang, Xinya Cao, Chenkai Guo, Jing Xu.
• code2vec: Learning Distributed Representations of Code Uri Alon, Omer Levy, Eran Yahav
• Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav
• 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
• 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

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

🏷 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
• Neural-Network Guided Expression Transformation Romain Edelmann, Viktor Kunčak
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
• DeepPERF: A Deep Learning-Based Approach For Improving Software Performance Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu
• 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
• 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
• Contrastive Code Representation Learning Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica
• 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
• Pre-trained Contextual Embedding of Source Code Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi
• SCELMo: Source Code Embeddings from Language Models Rafael-Michael Karampatsis, Charles Sutton
• Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• 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
• Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
• Unified Pre-training for Program Understanding and Generation Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• 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
• A User-Guided Approach to Program Analysis Ravi Mangal, Xin Zhang, Aditya V. Nori, Mayur Naik
• Learning a Strategy for Adapting a Program Analysis via Bayesian Optimisation Hakjoo Oh, Hongseok Yang, Kwangkeun Yi.
• 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
• Path-Based Function Embedding and its Application to Specification Mining Daniel DeFreez, Aditya V. Thakur, Cindy Rubio-González
• 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
• 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
• User-guided program reasoning using Bayesian inference Mukund Raghothaman, Sulekha Kulkarni, Kihong Heo, Mayur Naik
• Inferring Javascript types using Graph Neural Networks Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
• 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
• On the Feasibility of Transfer-learning Code Smells using Deep Learning Tushar Sharma, Vasiliki Efstathiou, Panos Louridas, Diomidis Spinellis
• Neural Program Repair by Jointly Learning to Localize and Repair Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh
• Neural Software Analysis Michael Pradel, Satish Chandra
• SinkFinder: harvesting hundreds of unknown interesting function pairs with just one seed Pan Bian, Bin Liang, Jianjun Huang, Wenchang Shi, Xidong Wang, Jian Zhang
• 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
• 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

🏷 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
• 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
• Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, 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
• OverCode: visualizing variation in student solutions to programming problems at scale Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller
• Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
• Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks Sahil Bhatia, Rishabh Singh
• sk_p: a neural program corrector for MOOCs Yewen Pu, Karthik Narasimhan, Armando Solar-Lezama, Regina Barzilay
• DeepFix: Fixing Common C Language Errors by Deep Learning Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade
• Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities Martin White, Michele Tufano, Matias Martinez, Martin Monperrus, Denys Poshyvanyk
• Semantic Code Repair using Neuro-Symbolic Transformation Networks Jacob Devlin, Jonathan Uesato, Rishabh Singh, Pushmeet Kohli
• 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
• Deep Reinforcement Learning for Programming Language Correction Rahul Gupta, Aditya Kanade, Shirish Shevade
• 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
• Neuro-symbolic program corrector for introductory programming assignments Sahil Bhatia, Pushmeet Kohli, Rishabh Singh
• Learning How to Mutate Source Code from Bug-Fixes Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• 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
• SampleFix: Learning to Correct Programs by Sampling Diverse Fixes Hossein Hajipour, Apratim Bhattacharyya, Cristian-Alexandru Staicu, Mario Fritz
• SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus
• Learning to Fix Build Errors with Graph2Diff Neural Networks Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
• DeepDelta: Learning to Repair Compilation Errors Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.
• On Learning Meaningful Code Changes via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• Neural Program Repair by Jointly Learning to Localize and Repair Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh
• DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
• 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
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
• Hoppity: Learning Bug Detection and Repair Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang
• 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
• Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing Haiwen Hong, Jingfeng Zhang, Yin Zhang, Yao Wan, Yulei Sui
• 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
• Neural Program Repair with Execution-based Backpropagation He Ye, Matias Martinez, Monperrus Martin
• Generating Bug-Fixes Using Pretrained Transformers Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan
• DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons Dawn Drain, Colin B. Clement, Guillermo Serrato, Neel Sundaresan
• DeepMerge: Learning to Merge Programs Elizabeth Dinella, Todd Mytkowicz, Alexey Svyatkovskiy, Christian Bird, Mayur Naik, Shuvendu K. Lahiri
• TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev
• Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
• 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
• Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models Iman Saberi, Fateme H. Fard
• TraceFixer: Execution Trace-Driven Program Repair Islem Bouzenia, Yangruibo Ding, Kexin Pei, Baishakhi Ray, Michael Pradel

🏷 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
• 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
• Toward Deep Learning Software Repositories Martin White, Christopher Vendome, Mario Linares-Vasquez, Denys Poshyvanyk
• Exploring the Use of Deep Learning for Feature Location Christopher S. Corley, Kostadin Damevski, Nicholas A. Kraft
• Automatically Learning Semantic Features for Defect Prediction Song Wang, Taiyue Liu, Lin Tan
• Learning API Usages from Bytecode: A Statistical Approach Tam The Nguyen, Hung Viet Pham, Phong Minh Vu, Tung Thanh Nguyen
• Automatically generating features for learning program analysis heuristics Kwonsoo Chae, Hakjoo Oh, Kihong Heo, Hongseok Yang
• Convolutional Neural Networks over Tree Structures for Programming Language Processing Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin
• Bugram: bug detection with n-gram language models Song Wang, Devin Chollak, Dana Movshovitz-Attias, Lin Tan
• Exploring API Embedding for API Usages and Applications Trong Duc Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Tien N. Nguyen
• 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
• SmartPaste: Learning to Adapt Source Code Miltiadis Allamanis, Marc Brockschmidt
• Path-Based Function Embedding and its Application to Specification Mining Daniel DeFreez, Aditya V. Thakur, Cindy Rubio-González
• Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks Nghi D. Q. Bui, Lingxiao Jiang, Yijun Yu
• Intelligent code reviews using deep learning Anshul Gupta, Neel Sundaresan
• Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code Nghi D. Q. Bui, Lingxiao Jiang
• Deep Learning Type Inference V. J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis
• Deep Learning Similarities from Different Representations of Source Code Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
• 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
• A General Path-Based Representation for Predicting Program Properties Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav
• Neural Code Comprehension: A Learnable Representation of Code Semantics Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
• A Literature Study of Embeddings on Source Code Zimin Chen, Martin Monperrus
• Mercem: Method Name Recommendation Based on Call Graph Embedding Hiroshi Yonai, Yasuhiro Hayase, Hiroyuki Kitagawa
• Program Classification Using Gated Graph Attention Neural Network for Online Programming Service Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
• A Novel Neural Source Code Representation based on Abstract Syntax Tree Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu
• Semantic Source Code Models Using Identifier Embeddings Vasiliki Efstathiou, Diomidis Spinellis
• 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 Execution through Neural Code Fusion Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
• code2vec: Learning Distributed Representations of Code Uri Alon, Omer Levy, Eran Yahav
• 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
• Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks Yi Li, Shaohua Wang, Tien N. Nguyen, Son Van Nguyen
• Import2vec - Learning Embeddings for Software Libraries Bart Theeten, Frederik Vandeputte, Tom Van Cutsem
• Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially Yudong Zhang, Wenhao Zheng, Ming Li
• TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
• Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta, Aditya Kanade, Shirish Shevade
• SAR: Learning Cross-Language API Mappings with Little Knowledge N. D. Q. Bui, Y. Yu, L. Jiang
• Capturing source code semantics via tree-based convolution over API-enhanced AST Long Chen, Wei Ye, Shikun Zhang
• Learning Scalable and Precise Representation of Program Semantics Ke Wang
• PathMiner : A Library for Mining of Path-Based Representations of Code Vladimir Kovalenko, Egor Bogomolov, Timofey Bryksin, Alberto Bacchelli.
• On the Feasibility of Transfer-learning Code Smells using Deep Learning Tushar Sharma, Vasiliki Efstathiou, Panos Louridas, Diomidis Spinellis
• code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
• Towards Learning Representations of Binary Executable Files for Security Tasks Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan
• Modular Tree Network for Source Code Representation Learning Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, Zhi Jin
• Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
• Contrastive Code Representation Learning Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
• Compiler-based graph representations for deep learning models of code Alexander Brauckmann, Andres Goens, Sebastian Ertel, Jeronimo Castrillon
• ComPy-Learn: A toolbox for exploring machine learning representations for compilers Alexander Brauckmann, Andrés Goens, Jeronimo Castrillon
• Searching a Database of Source Codes Using Contextualized Code Search Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
• InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• Unsupervised Learning of General-Purpose Embeddings for Code Changes Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev, Timofey Bryksin
• Multimodal Representation for Neural Code Search Jian Gu, Zimin Chen, Martin Monperrus
• 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
• Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
• 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
• CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik
• 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
• Topical: Learning Repository Embeddings from Source Code using Attention Agathe Lherondelle, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran
• An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
• 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

🏷 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
• 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.
• Neural Code Search Evaluation Dataset Hongyu Li, Seohyun Kim, Satish Chandra
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt
• CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning Ziyu Yao, Jayavardhan Reddy Peddamail, Huan Sun
• Neural query expansion for code search Jason Liu, Seohyun Kim, Vijayaraghavan Murali, Swarat Chaudhuri, Satish Chandra
• 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
• When Deep Learning Met Code Search Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, Satish Chandra
• Improving Code Search with Co-Attentive Representation Learning Jianhang Shuai, Ling Xu, Chao Liu, Meng Yan, Xin Xia, Yan Lei
• TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search Wenhua Wang, Yuqun Zhang, Zhengran Zeng, Guandong Xu
• Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent Geert Heyman, Tom Van Cutsem
• 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
• 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
• CoNCRA: A Convolutional Neural Network Code Retrieval Approach Marcelo de Rezende Martins, Marco Aurélio Gerosa
• 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
• Adaptive Deep Code Search Chunyang Ling, Zeqi Lin, Yanzhen Zou, Bing Xie
• A Multi-Perspective Architecture for Semantic Code Search Rajarshi Haldar, Lingfei Wu, Jinjun Xiong, Julia Hockenmaier
• OCoR: An Overlapping-Aware Code Retriever Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, Lu Zhang
• Searching a Database of Source Codes Using Contextualized Code Search Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
• PSCS: A Path-based Neural Model for Semantic Code Search Zhensu Sun, Yan Liu, Chen Yang, Yu Qian
• Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
• 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
• 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
• 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
• 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

🏷 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.
• Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu
• Predicting Vulnerability in Large Codebases With Deep Code Representation Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi
• 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
• Beware of the Unexpected: Bimodal Taint Analysis Yiu Wai Chow, Max Schäfer, Michael Pradel
• 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

🏷 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
• Abridging Source Code Binhang Yuan, Vijayaraghavan Murali, Christopher Jermaine
• Autofolding for Source Code Summarization Jaroslav Fowkes, Razan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton
• A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes Pablo Loyola, Edison Marrese-Taylor, Yutaka Matsuo
• A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
• CodeSum: Translate Program Language to Natural Language Xing Hu, Yuhan Wei, Ge Li, Zhi Jin
• 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
• Content Aware Source Code Change Description Generation Pablo Loyola, Edison Marrese-Taylor, Jorge Balazs, Yutaka Matsuo, Fumiko Satoh
• A Neural Model for Method Name Generation from Functional Description Sa Gao, Chunyang Chen, Zhenchang Xing, Yukun Ma, Wen Song, Shang-Wei Lin
• Structured Neural Summarization Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
• code2vec: Learning Distributed Representations of Code Uri Alon, Omer Levy, Eran Yahav
• Code Generation as a Dual Task of Code Summarization Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
• 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
• Commit Message Generation for Source Code Changes Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, Jian Lu
• Automatic Source Code Summarization with Extended Tree-LSTM Yusuke Shido, Yasuaki Kobayashi, Akihiro Yamamoto, Atsushi Miyamoto, Tadayuki Matsumura
• code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
• Improved Automatic Summarization of Subroutines via Attention to File Context Sakib Haque, Alexander LeClair, Lingfei Wu, Collin McMillan
• Learning to Represent Programs with Heterogeneous Graphs Wenhan Wang, Kechi Zhang, Ge Li, Zhi Jin
• A Transformer-based Approach for Source Code Summarization Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, Shikun Zhang
• 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
• PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
• CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs Yanlin Wang, Lun Du, Ensheng Shi, Yuxuan Hu, Shi Han, Dongmei Zhang
• Improved Code Summarization via a Graph Neural Network Alexander LeClair, Sakib Haque, Lingfei Wu, Collin McMillan
• Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• 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
• 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
• Learning code summarization from a small and local dataset Toufique Ahmed, Premkumar Devanbu
• LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
• 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
• 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
• A Survey on Deep Learning for Software Engineering Yanming Yang, Xin Xia, David Lo, John Grundy
• 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

🏷 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
• Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• Unit Test Case Generation with 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
• Semantic Scaffolds for Pseudocode-to-Code Generation Ruiqi Zhong, Mitchell Stern, Dan Klein
• 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
• 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
• A Conversational Paradigm for Program Synthesis Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong
• Grounded Copilot: How Programmers Interact with Code-Generating Models Shraddha Barke, Michael B. James, Nadia Polikarpova

🏷 test generation

• Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
• Unit Test Case Generation with 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

🏷 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
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback Mike Wu, Noah D. Goodman, Chris Piech, Chelsea Finn
• 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
• CoTexT: Multi-task Learning with Code-Text Transformer Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye
• Learning Type Annotation: Is Big Data Enough? Kevin Jesse, Premkumar Devanbu, Toufique Ahmed
• 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
• Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
• 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
• An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri
• 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
• Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation Gabriel Orlanski, Alex Gittens
• CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi
• 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
• Generating Bug-Fixes Using Pretrained Transformers Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan
• DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons Dawn Drain, Colin B. Clement, Guillermo Serrato, Neel Sundaresan
• Language-Agnostic Representation Learning of Source Code from Structure and Context Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann
• 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
• Distilling Transformers for Neural Cross-Domain Search Colin B. Clement, Chen Wu, Dawn Drain, Neel Sundaresan
• DIRECT : A Transformer-based Model for Decompiled Identifier Renaming Vikram Nitin, Anthony Saieva, Baishakhi Ray, Gail Kaiser
• Time-Efficient Code Completion Model for the R Programming Language Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev
• Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
• 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
• TreeBERT: A Tree-Based Pre-Trained Model for Programming Language Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, Lei Lyu
• 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
• CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
• Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
• Efficient Training of Language Models to Fill in the Middle Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen
• 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
• CoditT5: Pretraining for Source Code and Natural Language Editing Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Jessy Li, Milos Gligoric
• Code Translation with Compiler Representations Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, Gabriel Synnaeve
• An Extensive Study on Pre-trained Models for Program Understanding and Generation Zhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li, Yuqun Zhang, Lingming Zhang
• 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
• Synchromesh: Reliable code generation from pre-trained language models Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani
• A Systematic Evaluation of Large Language Models of Code Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn
• TOGA: A Neural Method for Test Oracle Generation Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, Shuvendu K. Lahiri
• Repository-Level Prompt Generation for Large Language Models of Code Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
• 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
• Learning to Complete Code with Sketches Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis
• Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes Cedric Richter, Heike Wehrheim
• SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, Bin Luo
• CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
• A Conversational Paradigm for Program Synthesis Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong
• Exploring and Evaluating Personalized Models for Code Generation Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin Clement, Neel Sundaresan, Michele Tufano
• Using Deep Learning to Generate Complete Log Statements Antonio Mastropaolo, Luca Pascarella, Gabriele Bavota
• 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
• ReACC: A Retrieval-Augmented Code Completion Framework Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, Alexey Svyatkovskiy
• DocCoder: Generating Code by Retrieving and Reading Docs Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao JIang, Graham Neubig
• Using Developer Discussions to Guide Fixing Bugs in Software Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney
• Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
• 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
• 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
• Learning to Reduce False Positives in Analytic Bug Detectors Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan
• 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
• 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
• 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
• 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
• Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models Iman Saberi, Fateme H. Fard
• TraceFixer: Execution Trace-Driven Program Repair Islem Bouzenia, Yangruibo Ding, Kexin Pei, Baishakhi Ray, Michael Pradel
• 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

🏷 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
• 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
• 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
• LambdaNet: Probabilistic Type Inference using Graph Neural Networks Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
• Adversarial Robustness for Code Pavol Bielik, Martin Vechev
• OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton
• Typilus: Neural Type Hints Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
• 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

🏷 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

• DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection Yizheng Chen, Zhoujie Ding, Xinyun Chen, David Wagner
• DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection Wenbo Wang, Tien N. Nguyen, Shaohua Wang, Yi Li, Jiyuan Zhang, Aashish Yadavally