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 editing education evaluation execution feature location fuzzing generalizability generation GNN grammar human evaluation information extraction instruction tuning interpretability language model large language models LLM logging memorization metrics migration naming natural language generation natural language processing notebook optimization pattern mining pretraining program analysis program synthesis question answering refactoring repair representation retrieval review search static analysis style summarization survey synthesis test generation tool topic modeling topic modelling traceability Transformer Transformers translation types variable misuse verification vulnerability
Tags
See below a list of all tags and the related papers
🏷 adversarial
- Adversarial Examples for Models of Code Noam Yefet, Uri Alon, Eran Yahav
- Generating Adversarial Examples for Holding Robustness of Source Code Processing Models Huangzhao Zhang, Zhuo Li, Ge Li, Lei Ma, Yang Liu, Zhi Jin
- Embedding Java Classes with code2vec: Improvements from Variable Obfuscation Rhys Compton, Eibe Frank, Panos Patros, Abigail Koay
- Adversarial Robustness for Code Pavol Bielik, Martin Vechev
- On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
- You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion Roei Schuster, Congzheng Song, Eran Tromer, Vitaly Shmatikov
- Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
- Semantic Robustness of Models of Source Code Jordan Henkel, Goutham Ramakrishnan, Zi Wang, Aws Albarghouthi, Somesh Jha, Thomas Reps
- Backdoors in Neural Models of Source Code Goutham Ramakrishnan, Aws Albarghouthi
🏷 API
- Lexical Statistical Machine Translation for Language Migration Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen
- Statistical Learning Approach for Mining API Usage Mappings for Code Migration Anh Tuan Nguyen, Hoan Anh Nguyen, Tung Thanh Nguyen, Tien N. Nguyen
- Parameter-Free Probabilistic API Mining across GitHub Jaroslav Fowkes, Charles Sutton
- Deep API Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim.
- Mapping API Elements for Code Migration with Vector Representations Trong Duc Nguyen, Anh Tuan Nguyen, Tien N. Nguyen
- Learning API Usages from Bytecode: A Statistical Approach Tam The Nguyen, Hung Viet Pham, Phong Minh Vu, Tung Thanh Nguyen
- Function Assistant: A Tool for NL Querying of APIs Kyle Richardson, Jonas Kuhn
- Learning Technical Correspondences in Technical Documentation Kyle Richardson, Jonas Kuhn
- Exploring API Embedding for API Usages and Applications Trong Duc Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Tien N. Nguyen
- DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim
- Finding Likely Errors with Bayesian Specifications Vijayaraghavan Murali, Swarat Chaudhuri, Chris Jermaine
- Polyglot Semantic Parsing in APIs Kyle Richardson, Jonathan Berant, Jonas Kuhn
- Bayesian Sketch Learning for Program Synthesis Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
- AutoPandas: neural-backed generators for program synthesis Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica
- Unsupervised Learning of API Aliasing Specifications Jan Eberhardt, Samuel Steffen, Veselin Raychev, Martin Vechev
- SAR: Learning Cross-Language API Mappings with Little Knowledge N. D. Q. Bui, Y. Yu, L. Jiang
- Mining Likely Analogical APIs across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding Chunyang Chen, Zhenchang Xing, Yang Liu, Kent Ong Long Xiong
🏷 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
- Neural Code Completion Chang Liu, Xin Wang, Richard Shin, Joseph E. Gonzalez, Dawn Song
- Learning Python Code Suggestion with a Sparse Pointer Network Avishkar Bhoopchand, Tim Rocktaschel, Earl Barr, Sebastian Riedel
- 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
- Code Prediction by Feeding Trees to Transformers Seohyun Kim, Jinman Zhao, Yuchi Tian, Satish Chandra
- Learning Autocompletion from Real-World Datasets Gareth Ari Aye, Seohyun Kim, Hongyu Li
- Sequence Model Design for Code Completion in the Modern IDE Gareth Ari Aye, Gail E. Kaiser
- A Structural Model for Contextual Code Changes Shaked Brody, Uri Alon, Eran Yahav
- On-the-Fly Adaptation of Source Code Models using Meta-Learning Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
- 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
- Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
- You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion Roei Schuster, Congzheng Song, Eran Tromer, Vitaly Shmatikov
- 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
- 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
- A Bimodal Modelling of Source Code and Natural Language Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
- Summarizing Source Code using a Neural Attention Model Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
- Latent Predictor Networks for Code Generation Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
- The Code2Text Challenge: Text Generation in Source Code Libraries Kyle Richardson, Sina Zarrieß, Jonas Kuhn
- Function Assistant: A Tool for NL Querying of APIs Kyle Richardson, Jonas Kuhn
- Learning Technical Correspondences in Technical Documentation Kyle Richardson, Jonas Kuhn
- A Syntactic Neural Model for General-Purpose Code Generation Pengcheng Yin, Graham Neubig
- Automatically Generating Commit Messages from Diffs using Neural Machine Translation Siyuan Jiang, Ameer Armaly, Collin McMillan
- pix2code: Generating Code from a Graphical User Interface Screenshot Tony Beltramelli
- Program Synthesis from Natural Language Using Recurrent Neural Networks Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, Michael D. Ernst
- CodeSum: Translate Program Language to Natural Language Xing Hu, Yuhan Wei, Ge Li, Zhi Jin
- A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
- Polyglot Semantic Parsing in APIs Kyle Richardson, Jonathan Berant, Jonas Kuhn
- Deep Learning to Detect Redundant Method Comments Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
- Mapping Language to Code in Programmatic Context Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
- 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
- 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
- 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
- Associating Natural Language Comment and Source Code Entities Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
- Deep Just-In-Time Inconsistency Detection Between Comments and Source Code Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
- TAG : Type Auxiliary Guiding for Code Comment Generation Ruichu Cai, Zhihao Liang, Boyan Xu, Zijian Li, Yuexing Hao, Yao Chen
- PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
- Code to Comment "Translation": Data, Metrics, Baselining & Evaluation David Gros, Hariharan Sezhiyan, Premkumar Devanbu, Zhou Yu
- 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
- 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.
- Deep Learning Similarities from Different Representations of Source Code Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
- Oreo: detection of clones in the twilight zone Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Pierre Baldi, Cristina Lopes
- Learning-based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection Lutz Büch, Artur Andrzejak
- 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
- Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree Wenhan Wang, Ge Li, Bo Ma, Xin Xia, Zhi Jin
- funcGNN: A Graph Neural Network Approach to Program Similarity Aravind Nair, Avijit Roy, Karl Meinke
- Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
- 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
🏷 code generation
- A Machine Learning Framework for Programming by Example Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, Adam Kalai
- Using Semantic Unification to Generate Regular Expressions from Natural Language Nate Kushman, Regina Barzilay
- Structured Generative Models of Natural Source Code Chris J. Maddison, Daniel Tarlow
- Phrase-Based Statistical Translation of Programming Languages S. Karaivanov, Veselin Raychev, Martin Vechev
- Code Completion with Statistical Language Models Veselin Raychev, Martin Vechev, Eran Yahav
- NLyze: Interactive Programming by Natural Language for SpreadSheet Data Analysis and Manipulation Sumit Gulwani, Mark Marron
- Visualizing and Understanding Recurrent Networks Andrej Karpathy, Justin Johnson, Li Fei-Fei
- Synthesizing Java expressions from free-form queries Tihomir Gvero, Viktor Kuncak
- PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
- A deep language model for software code Hoa Khanh Dam, Truyen Tran, Trang Pham
- Learning Programs from Noisy Data Veselin Raychev, Pavol lBielik, Martin Vechev, Andreas Krause
- Latent Predictor Networks for Code Generation Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
- Neural Attribute Machines for Program Generation Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
- Abstract Syntax Networks for Code Generation and Semantic Parsing Maxim Rabinovich, Mitchell Stern, Dan Klein
- Synthesizing benchmarks for predictive modeling Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather
- A Syntactic Neural Model for General-Purpose Code Generation Pengcheng Yin, Graham Neubig
- pix2code: Generating Code from a Graphical User Interface Screenshot Tony Beltramelli
- Program Synthesis from Natural Language Using Recurrent Neural Networks Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, Michael D. Ernst
- DeepFix: Fixing Common C Language Errors by Deep Learning Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade
- Compiler Fuzzing through Deep Learning Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather
- A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
- Mapping Language to Code in Programmatic Context Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
- NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, Michael D. Ernst
- Deep Reinforcement Learning for Programming Language Correction Rahul Gupta, Aditya Kanade, Shirish Shevade
- Generating Regular Expressions from Natural Language Specifications: Are We There Yet? Zexuan Zhong, Jiaqi Guo, Wei Yang, Tao Xie, Jian-Guang Lou, Ting Liu, Dongmei Zhang
- Learning to Generate Corrective Patches using Neural Machine Translation Hideaki Hata, Emad Shihab, Graham Neubig
- 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
- Bayesian Sketch Learning for Program Synthesis Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
- CODIT: Code Editing with Tree-Based Neural Machine Translation Saikat Chakraborty, Miltiadis Allamanis, Baishakhi Ray
- A Grammar-Based Structural CNN Decoder for Code Generation Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang
- Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
- SampleFix: Learning to Correct Programs by Sampling Diverse Fixes Hossein Hajipour, Apratim Bhattacharyya, Cristian-Alexandru Staicu, Mario Fritz
- Code Generation as a Dual Task of Code Summarization Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
- SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus
- A case study on machine learning for synthesizing benchmarks Andrés Goens, Alexander Brauckmann, Sebastian Ertel, Chris Cummins, Hugh Leather, Jeronimo Castrillon
- DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu
- 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
- Incorporating External Knowledge through Pre-training for Natural Language to Code Generation Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, Graham Neubig
- Unit Test Case Generation with Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
- Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
- Semantic Scaffolds for Pseudocode-to-Code Generation Ruiqi Zhong, Mitchell Stern, Dan Klein
- PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
- IntelliCode Compose: Code Generation Using Transformer Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan
- Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
- Energy-Based Models for Code Generation under Compilability Constraints Tomasz Korbak, Hady Elsahar, Marc Dymetman, Germán Kruszewski
- Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
- Time-Efficient Code Completion Model for the R Programming Language Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev
- Shellcode_IA32: A Dataset for Automatic Shellcode Generation Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella, Bojan Cukic, Samira Shaikh
- TOGA: A Neural Method for Test Oracle Generation Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, Shuvendu K. Lahiri
- Open-ended Knowledge Tracing Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew Lan
- 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
- RepoFusion: Training Code Models to Understand Your Repository Disha Shrivastava, Denis Kocetkov, Harm de Vries, Dzmitry Bahdanau, Torsten Scholak
🏷 dataset
- A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
- StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow Ziyu Yao, Daniel S. Weld, Wei-Peng Chen, Huan Sun
- Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow Pengcheng Yin, B. Deng, E. Chen, B. Vasilescu, Graham Neubig
- Public Git Archive: a Big Code dataset for all Vadim Markovtsev, Waren Long
- CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt
- Neural Code Search Evaluation Dataset Hongyu Li, Seohyun Kim, Satish Chandra
- 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
- CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
- 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
- Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation Gabriel Orlanski, Alex Gittens
- 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
- 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
- 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
- OctoPack: Instruction Tuning Code Large Language Models Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre
- Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
- DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection Yizheng Chen, Zhoujie Ding, Xinyun Chen, David Wagner
🏷 decompilation
- Learning to Align the Source Code to the Compiled Object Code Dor Levy, Lior Wolf
- Towards Neural Decompilation Omer Katz, Yuval Olshaker, Yoav Goldberg, Eran Yahav
- Coda: An End-to-End Neural Program Decompiler Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao
- DIRECT : A Transformer-based Model for Decompiled Identifier Renaming Vikram Nitin, Anthony Saieva, Baishakhi Ray, Gail Kaiser
- Code Translation with Compiler Representations Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, Gabriel Synnaeve
- LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang
🏷 defect
- Using Web Corpus Statistics for Program Analysis Chun-Hung Hsiao, Michael Cafarella, Satish Narayanasamy
- On the “Naturalness” of Buggy Code Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, Premkumar Devanbu
- 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
- Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
- Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
- Exploring the Naturalness of Buggy Code with Recurrent Neural Network Jack Lanchantin, Ji Gao
- Scalable Taint Specification Inference with Big Code V. Chibotaru, B. Bichsel, Veselin Raychev, Martin Vechev
- Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks Yi Li, Shaohua Wang, Tien N. Nguyen, Son Van Nguyen
- Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta, Aditya Kanade, Shirish Shevade
- OffSide: Learning to Identify Mistakes in Boundary Conditions Jón Arnar Briem, Jordi Smit, Hendrig Sellik, Pavel Rapoport, Georgios Gousios, Maurício Aniche.
- 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
- SCELMo: Source Code Embeddings from Language Models Rafael-Michael Karampatsis, Charles Sutton
- Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
- Co-Training for Commit Classification Jian Yi, David Lee, Hai Leong Chieu
- Deep Learning based Vulnerability Detection: Are We There Yet? Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, Baishakhi Ray
- On Distribution Shift in Learning-based Bug Detectors Jingxuan He, Luca Beurer-Kellner, Martin Vechev
- Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow
- Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes Cedric Richter, Heike Wehrheim
- Large Language Models and Simple, Stupid Bugs Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu, Emily Morgan
🏷 deobfuscation
- Predicting Program Properties from “Big Code” Veselin Raychev, Martin Vechev, Andreas Krause
- Statistical Deobfuscation of Android Applications Benjamin Bichsel, Veselin Raychev, Petar Tsankov, Martin Vechev
- Towards Better Program Obfuscation: Optimization via Language Models Han Liu
- Recovering Clear, Natural Identifiers from Obfuscated JS Names Bogdan Vasilescu, Casey Casalnuovo, Premkumar Devanbu
- Recovering Variable Names for Minified Code with Usage Contexts Hieu Tran, Ngoc Tran, Son Nguyen, Hoan Nguyen, Tien N. Nguyen
- A Neural Approach to Decompiled Identifier Renaming Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu
- Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav
🏷 documentation
- Natural Language Models for Predicting Programming Comments Dana Movshovitz-Attias, William W. Cohen
- A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
- Learning Technical Correspondences in Technical Documentation Kyle Richardson, Jonas Kuhn
- Deep Learning to Detect Redundant Method Comments Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
- Improving Automatic Source Code Summarization via Deep Reinforcement Learning Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu
- A Neural Model for Generating Natural Language Summaries of Program Subroutines Alexander LeClair, Siyuan Jiang, Collin McMillan
- Structured Neural Summarization Patrick Fernandes, 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
- Automating Just-In-Time Comment Updating Zhongxin Liu, Xin Xia, Meng Yan, Shanping Li
- TAG : Type Auxiliary Guiding for Code Comment Generation Ruichu Cai, Zhihao Liang, Boyan Xu, Zijian Li, Yuexing Hao, Yao Chen
- PyMT5: multi-mode translation of natural language and Python code with transformers Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan
- Code to Comment "Translation": Data, Metrics, Baselining & Evaluation David Gros, Hariharan Sezhiyan, Premkumar Devanbu, Zhou Yu
- TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search Wenhua Wang, Yuqun Zhang, Zhengran Zeng, Guandong Xu
- 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
- 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
- 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
- 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
🏷 edit
- A Study of Repetitiveness of Code Changes in Software Evolution Hoan Anh Nguyen, Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen, and Hridesh Rajan
- Automatically Generating Commit Messages from Diffs using Neural Machine Translation Siyuan Jiang, Ameer Armaly, Collin McMillan
- A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes Pablo Loyola, Edison Marrese-Taylor, Yutaka Matsuo
- 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
- Learning How to Mutate Source Code from Bug-Fixes Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
- Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change Patterns Hoan Anh Nguyen, Tien N. Nguyen, Danny Dig, Son Nguyen, Hieu Tran, and Michael Hilton
- On Learning Meaningful Code Changes via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
- Commit Message Generation for Source Code Changes Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, Jian Lu
- 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.
- Learning to Represent Edits Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
- Commit2Vec: Learning Distributed Representations of Code Changes Adelina Ciurumelea; Sebastian Proksch; Harald C. Gall
- Generating commit messages from diffs using pointer-generator network Qin Liu, Zihe Liu, Hongming Zhu, Hongfei Fan, Bowen Du, Yu Qian.
- Neural Networks for Modeling Source Code Edits Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow
- Copy that! Editing Sequences by Copying Spans Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt
- Hoppity: Learning Bug Detection and Repair Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang
- 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
- DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
- Learning to Update Natural Language Comments Based on Code Changes Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li
- CC2Vec: Distributed Representations of Code Changes Thong Hoang, Hong Jin Kang, Julia Lawall, David Lo
- 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
- Jointly Learning to Repair Code and Generate Commit Message Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li
- A Semantic Bug Seeding: A Learning-Based Approach for Creating Realistic Bugs Jibesh Patra, Michael Pradel
- DeepMerge: Learning to Merge Programs Elizabeth Dinella, Todd Mytkowicz, Alexey Svyatkovskiy, Christian Bird, Mayur Naik, Shuvendu K. Lahiri
- On Multi-Modal Learning of Editing Source Code Saikat Chakraborty, Baishakhi Ray
- A Syntax-Guided Edit Decoder for Neural Program Repair Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, Lu Zhang
- Learning to Model Editing Processes Machel Reid, Graham Neubig
- CoditT5: Pretraining for Source Code and Natural Language Editing Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Jessy Li, Milos Gligoric
🏷 editing
🏷 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
- 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
- Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
- On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
- Impact of Evaluation Methodologies on Code Summarization Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, Milos Gligoric
- Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
- CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code Aryaz Eghbali, Michael Pradel
- Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
- Semantic Similarity Metrics for Evaluating Source Code Summarization Sakib Haque, Zachary Eberhart, Aakash Bansal, Collin McMillan
- Human perceiving behavior modeling in evaluation of code generation models S. Kovalchuk, V. Lomshakov, A. Aliev
- Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
- Natural Language to Code Generation in Interactive Data Science Notebooks Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton
- Productivity Assessment of Neural Code Completion Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew Rice, Devon Rifkin, Edward Aftandilian
- An Extensive Study on Pre-trained Models for Program Understanding and Generation Zhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li, Yuqun Zhang, Lingming Zhang
- CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code Shuyan Zhou, Uri Alon, Sumit Agarwal, Graham Neubig
- Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev
- Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
- CodeScore: Evaluating Code Generation by Learning Code Execution Yihong Dong, Jiazheng Ding, Xue Jiang, Zhuo Li, Ge Li, Zhi Jin
- LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang
🏷 execution
- Learning to Execute Wojciech Zaremba, Ilya Sutskever
- Show Your Work: Scratchpads for Intermediate Computation with Language Models Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena
- SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics He Ye, Matias Martinez, Xiapu Luo, Tao Zhang, Martin Monperrus
- CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
- Code Execution with Pre-trained Language Models Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan
- LExecutor: Learning-Guided Execution Beatriz Souza, Michael Pradel
🏷 feature location
🏷 fuzzing
- Learning to Fuzz: Application-Independent Fuzz Testing with Probabilistic, Generative Models of Input Data Jibesh Patra, Michael Pradel
- Compiler Fuzzing through Deep Learning Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather
- NEUZZ: Efficient Fuzzing with Neural Program Smoothing Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana
- DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu
- Learning to Fuzz from Symbolic Execution with Application to Smart Contracts Jingxuan He, Mislav Balunović, Nodar Ambroladze, Petar Tsankov, Martin Vechev
- Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer Suyoung Lee, HyungSeok Han, Sang Kil Cha, Sooel Son
- Universal Fuzzing via Large Language Models Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian, Michael Pradel, Lingming Zhang
🏷 generalizability
- On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
- Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
- Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff
🏷 generation
🏷 GNN
- Gated Graph Sequence Neural Networks Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
- Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
- Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
- Inferring Javascript types using Graph Neural Networks Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
- AutoPandas: neural-backed generators for program synthesis Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica
- 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
- Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav
- Structured Neural Summarization Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
- Program Classification Using Gated Graph Attention Neural Network for Online Programming Service Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
- Learning to Fuzz from Symbolic Execution with Application to Smart Contracts Jingxuan He, Mislav Balunović, Nodar Ambroladze, Petar Tsankov, Martin Vechev
- Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov
- 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
- LambdaNet: Probabilistic Type Inference using Graph Neural Networks Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
- Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu
- Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
- Typilus: Neural Type Hints Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
- Learning Graph Structure With A Finite-State Automaton Layer Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
- ProGraML: Graph-based Deep Learning for Program Optimization and Analysis Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather
- Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree Wenhan Wang, Ge Li, Bo Ma, Xin Xia, Zhi Jin
- funcGNN: A Graph Neural Network Approach to Program Similarity Aravind Nair, Avijit Roy, Karl Meinke
- Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
- 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
- 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
- Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
- Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
🏷 grammar
- Structured Statistical Syntax Tree Prediction Cyrus Omar
- Mining Idioms from Source Code Miltiadis Allamanis, Charles Sutton
- Structured Generative Models of Natural Source Code Chris J. Maddison, Daniel Tarlow
- Building Program Vector Representations for Deep Learning Hao Peng, Lili Mou, Ge Li, Yuxuan Liu, Lu Zhang, Zhi Jin.
- 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
- A Bimodal Modelling of Source Code and Natural Language Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
- PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
- Convolutional Neural Networks over Tree Structures for Programming Language Processing Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin
- Learning Programs from Noisy Data Veselin Raychev, Pavol lBielik, Martin Vechev, Andreas Krause
- 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
- Abstract Syntax Networks for Code Generation and Semantic Parsing Maxim Rabinovich, Mitchell Stern, Dan Klein
- A Syntactic Neural Model for General-Purpose Code Generation Pengcheng Yin, Graham Neubig
- 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
- A Grammar-Based Structural CNN Decoder for Code Generation Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang
- Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
- 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
- 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.
- A Novel Neural Source Code Representation based on Abstract Syntax Tree Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu
- 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 Structural Model for Contextual Code Changes Shaked Brody, Uri Alon, Eran Yahav
- Modular Tree Network for Source Code Representation Learning Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, Zhi Jin
- Predicting Vulnerability in Large Codebases With Deep Code Representation Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi
- DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
- 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
- Grounded Copilot: How Programmers Interact with Code-Generating Models Shraddha Barke, Michael B. James, Nadia Polikarpova
- Semantic Similarity Metrics for Evaluating Source Code Summarization Sakib Haque, Zachary Eberhart, Aakash Bansal, Collin McMillan
- Human perceiving behavior modeling in evaluation of code generation models S. Kovalchuk, V. Lomshakov, A. Aliev
- Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models Priyan Vaithilingam, Tianyi Zhang, Elena Glassman
- 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
🏷 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
🏷 instruction tuning
🏷 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
- Structured Statistical Syntax Tree Prediction Cyrus Omar
- Mining Source Code Repositories at Massive Scale Using Language Modeling Miltiadis Allamanis, Charles Sutton
- A Statistical Semantic Language Model for Source Code Tung Thanh Nguyen, Anh Tuan Nguyen, Hoan Anh Nguyen, Tien N. Nguyen
- 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
- On the Localness of Software Zhaopeng Tu, Zhendong Su, Premkumar Devanbu
- Structured Generative Models of Natural Source Code Chris J. Maddison, Daniel Tarlow
- Code Completion with Statistical Language Models Veselin Raychev, Martin Vechev, Eran Yahav
- Will they like this? Evaluating Code Contributions With Language Models Vincent J. Hellendoorn, Premkumar Devanbu, Alberto Bacchelli
- 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
- CACHECA: A Cache Language Model Based Code Suggestion Tool Christine Franks, Zhaopeng Tu, Premkumar Devanbu, Vincent Hellendoorn
- Products, Developers, and Milestones: How Should I Build My N-Gram Language Model Juliana Saraiva, Christian Bird, Thomas Zimmermann
- PHOG: Probabilistic Model for Code Pavol Bielik, Veselin Raychev, Martin Vechev
- Learning Python Code Suggestion with a Sparse Pointer Network Avishkar Bhoopchand, Tim Rocktaschel, Earl Barr, Sebastian Riedel
- A deep language model for software code Hoa Khanh Dam, Truyen Tran, Trang Pham
- A Language Model for Statements of Software Code Yixiao Yang, Yu Jiang, Ming Gu, Jiaguang Sun, Jian Gao, Han Liu
- Are Deep Neural Networks the Best Choice for Modeling Source Code? Vincent J. Hellendoorn, Premkumar Devanbu
- Code Completion with Neural Attention and Pointer Networks Jian Li, Yue Wang, Michael R. Lyu, Irwin King
- 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
- Pythia: AI-assisted Code Completion System Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, Neel Sundaresan
- Maybe Deep Neural Networks are the Best Choice for Modeling Source Code Rafael-Michael Karampatsis, Charles Sutton
- 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
- CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
- Evaluating Large Language Models Trained on Code Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, Will Guss, Alex Nichol, Igor Babuschkin, Suchir Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba
- Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models Jingxuan Li, Rui Huang, Wei Li, Kai Yao, Weiguo Tan
- An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri
- Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
- 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
- Neural Program Generation Modulo Static Analysis Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Chris Jermaine
- On the Naturalness and Localness of Software Logs Sina Gholamian, Paul A. S. Ward
- 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
- Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
- Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, Xiangke Liao
- Learning to Complete Code with Sketches Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis
- Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
- LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
- An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
- Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models Priyan Vaithilingam, Tianyi Zhang, Elena Glassman
- Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
🏷 large language models
- Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets V. Lomshakov, S. Kovalchuk, M. Omelchenko, S. Nikolenko, A. Aliev
- Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
- (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
- Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search Haochen Li, Xin Zhou, Zhiqi Shen
- LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang
🏷 LLM
- A Static Evaluation of Code Completion by Large Language Models Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
- LLM4Decompile: Decompiling Binary Code with Large Language Models Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang
🏷 logging
🏷 memorization
🏷 metrics
🏷 migration
- 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
- 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
- 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
- Suggesting Accurate Method and Class Names Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
- 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
- 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
- Neural Reverse Engineering of Stripped Binaries Yaniv David, Uri Alon, Eran Yahav
- Method name suggestion with hierarchical attention networks Sihan Xu, Sen Zhang, Weijing Wang, Xinya Cao, Chenkai Guo, Jing Xu.
- 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
- A Neural Model for Method Name Generation from Functional Description Sa Gao, Chunyang Chen, Zhenchang Xing, Yukun Ma, Wen Song, Shang-Wei Lin
- code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
- code2vec: Learning Distributed 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
- Embedding Java Classes with code2vec: Improvements from Variable Obfuscation Rhys Compton, Eibe Frank, Panos Patros, Abigail Koay
- Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
- 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
🏷 natural language processing
🏷 notebook
- Natural Language to Code Generation in Interactive Data Science Notebooks Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton
🏷 optimization
- End-to-end Deep Learning of Optimization Heuristics Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather
- Synthesizing benchmarks for predictive modeling Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather
- Neural-Network Guided Expression Transformation Romain Edelmann, Viktor Kunčak
- Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
- 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
- Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
- Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Giuseppe Tagliavini, Andrea Acquaviva
- Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva
- Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
- DeepPERF: A Deep Learning-Based Approach For Improving Software Performance Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu
- Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
- Supersonic: Learning to Generate Source Code Optimizations in C/C++ Zimin Chen, Sen Fang, Martin Monperrus
🏷 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
- Learning Programmatic Idioms for Scalable Semantic Parsing Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
- 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
- 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
- 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
- Contrastive Code Representation Learning Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica
- SCELMo: Source Code Embeddings from Language Models Rafael-Michael Karampatsis, Charles Sutton
- Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
- DOBF: A Deobfuscation Pre-Training Objective for Programming Languages Baptiste Roziere, Marie-Anne Lachaux, Marc Szafraniec, Guillaume Lample
- Unified Pre-training for Program Understanding and Generation Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
- 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
- Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
- An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
- What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
🏷 program analysis
- A Factor Graph Model for Software Bug Finding Ted Kremenek, Andrew Y. Ng, Dawson R. Engler.
- 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.
- Predicting Program Properties from “Big Code” Veselin Raychev, Martin Vechev, Andreas Krause
- Gated Graph Sequence Neural Networks Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
- Deep Learning to Find Bugs Michael Pradel, Koushik Sen
- Finding Likely Errors with Bayesian Specifications Vijayaraghavan Murali, Swarat Chaudhuri, Chris Jermaine
- User-guided program reasoning using Bayesian inference Mukund Raghothaman, Sulekha Kulkarni, Kihong Heo, Mayur Naik
- 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
- 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
- Scalable Taint Specification Inference with Big Code V. Chibotaru, B. Bichsel, Veselin Raychev, Martin Vechev
- 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
- Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
- Neural Bug Finding: A Study of Opportunities and Challenges Andrew Habib, Michael Pradel
- 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
- Learning Graph Structure With A Finite-State Automaton Layer Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
- Predicting Vulnerability in Large Codebases With Deep Code Representation Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi
- SinkFinder: harvesting hundreds of unknown interesting function pairs with just one seed Pan Bian, Bin Liang, Jianjun Huang, Wenchang Shi, Xidong Wang, Jian Zhang
- Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
- Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Giuseppe Tagliavini, Andrea Acquaviva
- Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
- What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
- Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
- (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
🏷 program synthesis
🏷 question answering
🏷 refactoring
- Testing Neural Program Analyzers Md Rafiqul Islam Rabin, Ke Wang, Mohammad Amin Alipour
- Mercem: Method Name Recommendation Based on Call Graph Embedding Hiroshi Yonai, Yasuhiro Hayase, Hiroyuki Kitagawa
- On the Impact of Refactoring Operations on Code Naturalness Bin Lin, Csaba Nagy, Gabriele Bavota, Michele Lanza
- Recommendation of Move Method Refactoring Using Path-Based Representation of Code Zarina Kurbatova, Ivan Veselov, Yaroslav Golubev, Timofey Bryksin
- Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour
- On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour
- Mining Idioms in the Wild Aishwarya Sivaraman, Rui Abreu, Andrew Scott, Tobi Akomolede, Satish Chandra
- Memorization and Generalization in Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
- Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
🏷 repair
- Syntax Errors Just Aren’t Natural: Improving Error Reporting with Language Models Joshua Charles Campbell, Abram Hindle, José Nelson Amaral
- Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
- OverCode: visualizing variation in student solutions to programming problems at scale Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller
- 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
- 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
- DeepFix: Fixing Common C Language Errors by Deep Learning Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade
- 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
- Neuro-symbolic program corrector for introductory programming assignments Sahil Bhatia, Pushmeet Kohli, Rishabh Singh
- 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
- Learning How to Mutate Source Code from Bug-Fixes Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
- Learning to Generate Corrective Patches using Neural Machine Translation Hideaki Hata, Emad Shihab, Graham Neubig
- 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
- 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
- On Learning Meaningful Code Changes via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
- Learning to Fix Build Errors with Graph2Diff Neural Networks Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
- SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus
- DeepDelta: Learning to Repair Compilation Errors Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.
- Neural Program Repair by Jointly Learning to Localize and Repair Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh
- Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair Haoye Tian, Kui Liu, Abdoul Kader Kaboreé, Anil Koyuncu, Li Li, Jacques Klein, Tegawendé F. Bissyandé
- Hoppity: Learning Bug Detection and Repair Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang
- Graph-based, Self-Supervised Program Repair from Diagnostic Feedback Michihiro Yasunaga, Percy Liang
- DLFix: Context-based Code Transformation Learning for Automated Program Repair Yi Li, Shaohua Wang, Tien N. Nguyen
- Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
- 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
- A Semantic Bug Seeding: A Learning-Based Approach for Creating Realistic Bugs Jibesh Patra, Michael Pradel
- Learning to Find Naming Issues with Big Code and Small Supervision Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin Vechev
- Neural Program Repair with Execution-based Backpropagation He Ye, Matias Martinez, Monperrus Martin
- Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing Haiwen Hong, Jingfeng Zhang, Yin Zhang, Yao Wan, Yulei Sui
- DeepMerge: Learning to Merge Programs Elizabeth Dinella, Todd Mytkowicz, Alexey Svyatkovskiy, Christian Bird, Mayur Naik, Shuvendu K. Lahiri
- DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons Dawn Drain, Colin B. Clement, Guillermo Serrato, Neel Sundaresan
- Generating Bug-Fixes Using Pretrained Transformers Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan
- Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
- TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev
- SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics He Ye, Matias Martinez, Xiapu Luo, Tao Zhang, Martin Monperrus
- Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes Cedric Richter, Heike Wehrheim
- Using Developer Discussions to Guide Fixing Bugs in Software Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney
- Demystifying GPT Self-Repair for Code Generation Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, Armando Solar-Lezama
- SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili, Moshi Wei, Alvine Boaye Belle, Hung Viet Pham, Song Wang
- 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
- RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair André Silva, Sen Fang, Martin Monperrus
- T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble Reza Gharibi, Mohammad Hadi Sadreddini, Seyed Mostafa Fakhrahmad
🏷 representation
- Learning to Execute Wojciech Zaremba, Ilya Sutskever
- Building Program Vector Representations for Deep Learning Hao Peng, Lili Mou, Ge Li, Yuxuan Liu, Lu Zhang, Zhi Jin.
- Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
- 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
- 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
- Automatically generating features for learning program analysis heuristics Kwonsoo Chae, Hakjoo Oh, Kihong Heo, Hongseok Yang
- Automatically Learning Semantic Features for Defect Prediction Song Wang, Taiyue Liu, Lin Tan
- Learning API Usages from Bytecode: A Statistical Approach Tam The Nguyen, Hung Viet Pham, Phong Minh Vu, Tung Thanh Nguyen
- Bugram: bug detection with n-gram language models Song Wang, Devin Chollak, Dana Movshovitz-Attias, Lin Tan
- Convolutional Neural Networks over Tree Structures for Programming Language Processing Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin
- Neural Attribute Machines for Program Generation Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
- Exploring API Embedding for API Usages and Applications Trong Duc Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Tien N. Nguyen
- SmartPaste: Learning to Adapt Source Code Miltiadis Allamanis, Marc Brockschmidt
- Semantically enhanced software traceability using deep learning techniques Jin Guo, Jinghui Cheng, Jane Cleland-Huang
- Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code Nghi D. Q. Bui, Lingxiao Jiang
- 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
- 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
- Intelligent code reviews using deep learning Anshul Gupta, Neel Sundaresan
- Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
- Deep Learning Similarities from Different Representations of Source Code Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk
- Neural Code Comprehension: A Learnable Representation of Code Semantics Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
- Deep Learning Type Inference V. J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis
- Learning Execution through Neural Code Fusion Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
- 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
- SAR: Learning Cross-Language API Mappings with Little Knowledge N. D. Q. Bui, Y. Yu, L. Jiang
- Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks Yi Li, Shaohua Wang, Tien N. Nguyen, Son Van Nguyen
- 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
- Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta, Aditya Kanade, Shirish Shevade
- 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
- PathMiner : A Library for Mining of Path-Based Representations of Code Vladimir Kovalenko, Egor Bogomolov, Timofey Bryksin, Alberto Bacchelli.
- 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
- Learning Scalable and Precise Representation of Program Semantics Ke Wang
- On the Feasibility of Transfer-learning Code Smells using Deep Learning Tushar Sharma, Vasiliki Efstathiou, Panos Louridas, Diomidis Spinellis
- Program Classification Using Gated Graph Attention Neural Network for Online Programming Service Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
- code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
- A Novel Neural Source Code Representation based on Abstract Syntax Tree Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu
- code2vec: Learning Distributed Representations of Code Uri Alon, Omer Levy, Eran Yahav
- Capturing source code semantics via tree-based convolution over API-enhanced AST Long Chen, Wei Ye, Shikun Zhang
- Towards Learning Representations of Binary Executable Files for Security Tasks Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan
- Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
- Searching a Database of Source Codes Using Contextualized Code Search Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
- Towards Demystifying Dimensions of Source Code Embeddings Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
- Modular Tree Network for Source Code Representation Learning Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, Zhi Jin
- 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
- Contrastive Code Representation Learning Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica
- Unsupervised Learning of General-Purpose Embeddings for Code Changes Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev, Timofey Bryksin
- Disentangled Code Representation Learning for Multiple Programming Languages Jingfeng Zhang, Haiwen Hong, Yin Zhang, Yao Wan, Ye Liu, Yulei Sui
- 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
- IdBench: Evaluating Semantic Representations of Identifier Names in Source Code Yaza Wainakh, Moiz Rauf, Michael Pradel
- InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
- Learning Program Semantics with Code Representations: An Empirical Study Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu
- Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, Xiangke Liao
- SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, Bin Luo
- LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
- An Exploratory Study on Code Attention in BERT Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo
- CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik
- Topical: Learning Repository Embeddings from Source Code using Attention Agathe Lherondelle, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran
- Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
🏷 retrieval
- Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
- RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
🏷 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
- Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva
- What is it like to program with artificial intelligence? Advait Sarkar, Andrew D. Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, Ben Zorn
🏷 search
- Aroma: code recommendation via structural code search Sifei Luan, Di Yang, Celeste Barnaby, Koushik Sen, Satish Chandra
- A Bimodal Modelling of Source Code and Natural Language Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
- Deep API Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim.
- A Retrieve-and-Edit Framework for Predicting Structured Outputs Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
- Deep Code Search Xiaodong Gu, Hongyu Zhang, Sunghun Kim.
- CodeSearchNet Challenge: Evaluating the State of Semantic Code Search Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt
- Neural Code Search Evaluation Dataset Hongyu Li, Seohyun Kim, Satish Chandra
- 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
- CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning Ziyu Yao, Jayavardhan Reddy Peddamail, Huan Sun
- When Deep Learning Met Code Search Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, Satish Chandra
- 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
- CoNCRA: A Convolutional Neural Network Code Retrieval Approach Marcelo de Rezende Martins, Marco Aurélio Gerosa
- Searching a Database of Source Codes Using Contextualized Code Search Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
- Improving Code Search with Co-Attentive Representation Learning Jianhang Shuai, Ling Xu, Chao Liu, Meng Yan, Xin Xia, Yan Lei
- Learning Code-Query Interaction for Enhancing Code Searches Wei Li, Haozhe Qin, Shuhan Yan, Beijun Shen, Yuting Chen
- Adaptive Deep Code Search Chunyang Ling, Zeqi Lin, Yanzhen Zou, Bing Xie
- TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search Wenhua Wang, Yuqun Zhang, Zhengran Zeng, Guandong Xu
- 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
- PSCS: A Path-based Neural Model for Semantic Code Search Zhensu Sun, Yan Liu, Chen Yang, Yu Qian
- 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
- OCoR: An Overlapping-Aware Code Retriever Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, Lu Zhang
- A Multi-Perspective Architecture for Semantic Code Search Rajarshi Haldar, Lingfei Wu, Jinjun Xiong, Julia Hockenmaier
- 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
- Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
- Exploring Representation-Level Augmentation for Code Search Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang
- Senatus - A Fast and Accurate Code-to-Code Recommendation Engine Fran Silavong, Sean Moran, Antonios Georgiadis, Rohan Saphal, Robert Otter
- DocCoder: Generating Code by Retrieving and Reading Docs Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao JIang, Graham Neubig
- CodeDSI: Differentiable Code Search Usama Nadeem, Noah Ziems, Shaoen Wu
- Rethinking Negative Pairs in Code Search Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
- Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search Haochen Li, Xin Zhou, Zhiqi Shen
🏷 static analysis
- Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools Ugur Koc, Parsa Saadatpanah, Jeffrey S. Foster, Adam A. Porter.
- Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
- 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
- Exploration of Convolutional Neural Network models for source code classification Francesco Barchi, Emanuele Parisi, Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva
- Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Andrea Acquaviva
- Learning to Answer Semantic Queries over Code Surya Prakash Sahu, Madhurima Mandal, Shikhar Bharadwaj, Aditya Kanade, Petros Maniatis, Shirish Shevade
- Learning to Reduce False Positives in Analytic Bug Detectors Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan
- Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
- The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models Haonan Li, Yu Hao, Yizhuo Zhai, Zhiyun Qian
- A Static Evaluation of Code Completion by Large Language Models Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng Li, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
- (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
- Beware of the Unexpected: Bimodal Taint Analysis Yiu Wai Chow, Max Schäfer, Michael Pradel
🏷 style
🏷 summarization
- Natural Language Models for Predicting Programming Comments Dana Movshovitz-Attias, William W. Cohen
- Summarizing Source Code using a Neural Attention Model Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
- A Convolutional Attention Network for Extreme Summarization of Source Code Miltiadis Allamanis, Hao Peng, Charles Sutton
- Abridging Source Code Binhang Yuan, Vijayaraghavan Murali, Christopher Jermaine
- A parallel corpus of Python functions and documentation strings for automated code documentation and code generation Antonio Valerio Miceli Barone, Rico Sennrich
- 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
- CodeSum: Translate Program Language to Natural Language Xing Hu, Yuhan Wei, Ge Li, Zhi Jin
- 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
- Improving Automatic Source Code Summarization via Deep Reinforcement Learning Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu
- Automatic Source Code Summarization with Extended Tree-LSTM Yusuke Shido, Yasuaki Kobayashi, Akihiro Yamamoto, Atsushi Miyamoto, Tadayuki Matsumura
- Commit Message Generation for Source Code Changes Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, Jian Lu
- Code Generation as a Dual Task of Code Summarization Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
- A Neural Model for Generating Natural Language Summaries of Program Subroutines Alexander LeClair, Siyuan Jiang, Collin McMillan
- Structured Neural Summarization Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
- Recommendations for Datasets for Source Code Summarization Alexander LeClair, Collin McMillan
- A Neural Model for Method Name Generation from Functional Description Sa Gao, Chunyang Chen, Zhenchang Xing, Yukun Ma, Wen Song, Shang-Wei Lin
- code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Omer Levy, Eran Yahav
- code2vec: Learning Distributed 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
- 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
- Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, Shikun Zhang
- Improved Code Summarization via a Graph Neural Network Alexander LeClair, Sakib Haque, Lingfei Wu, Collin McMillan
- NaturalCC: A Toolkit to Naturalize the Source Code Corpus Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu
- 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
- LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
- Learning code summarization from a small and local dataset Toufique Ahmed, Premkumar Devanbu
- Improving Few-Shot Prompts with Relevant Static Analysis Products Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, Earl T. Barr
- Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models Iman Saberi, Fateme H. Fard
🏷 survey
- Neural Software Analysis Michael Pradel, Satish Chandra
- A Survey on Deep Learning for Software Engineering Yanming Yang, Xin Xia, David Lo, John Grundy
- Deep Learning & Software Engineering: State of Research and Future Directions Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang
- Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors Junayed Mahmud, Fahim Faisal, Raihan Islam Arnob, Antonios Anastasopoulos, Kevin Moran
- A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, Denys Poshyvanyk
- Deep Learning based Vulnerability Detection: Are We There Yet? Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, Baishakhi Ray
🏷 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
- AutoPandas: neural-backed generators for program synthesis Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica
- SPoC: Search-based Pseudocode to Code Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy S. Liang
- Unit Test Case Generation with Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
- Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
- Semantic Scaffolds for Pseudocode-to-Code Generation Ruiqi Zhong, Mitchell Stern, Dan Klein
- IntelliCode Compose: Code Generation Using Transformer Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan
- Evaluating Large Language Models Trained on Code Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, Will Guss, Alex Nichol, Igor Babuschkin, Suchir Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba
- DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum
- Program Synthesis with Large Language Models Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton
- A large-scale benchmark for few-shot program induction and synthesis Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, Joshua Tenenbaum
- Leveraging Language to Learn Program Abstractions and Search Heuristics Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum, Jacob Andreas
- Neural Program Generation Modulo Static Analysis Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Chris Jermaine
- A Conversational Paradigm for Program Synthesis Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong
- I Speak, You Verify: Toward Trustworthy Neural Program Synthesis Darren Key, Wen-Ding Li, Kevin Ellis
- CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
- Grounded Copilot: How Programmers Interact with Code-Generating Models Shraddha Barke, Michael B. James, Nadia Polikarpova
🏷 test generation
- Unit Test Case Generation with Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
- Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan
- TOGA: A Neural Method for Test Oracle Generation Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, Shuvendu K. Lahiri
- Test-based and metric-based evaluation of code generation models for practical question answering S. Kovalchuk, D. Fedrushkov, V. Lomshakov, A. Aliev
🏷 tool
- PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code Egor Spirin, Egor Bogomolov, Vladimir Kovalenko, Timofey Bryksin
- Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, Sriram Rajamani
- (Partial) Program Dependence Learning Aashish Yadavally, Wenbo Wang, Shaohua Wang, Tien N. Nguyen
🏷 topic modeling
🏷 topic modelling
🏷 traceability
🏷 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
- Empirical Study of Transformers for Source Code Nadezhda Chirkova, Sergey Troshin
- Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishab Singh, Petros Maniatis, David Bieber
- Self-Supervised Bug Detection and Repair Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
- CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model Tae Hwan Jung
- 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
- CoTexT: Multi-task Learning with Code-Text Transformer Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye
- 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
- Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors Junayed Mahmud, Fahim Faisal, Raihan Islam Arnob, Antonios Anastasopoulos, Kevin Moran
- Learning Type Annotation: Is Big Data Enough? Kevin Jesse, Premkumar Devanbu, Toufique Ahmed
- 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
- 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
- An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri
- Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation Gabriel Orlanski, Alex Gittens
- DIRECT : A Transformer-based Model for Decompiled Identifier Renaming Vikram Nitin, Anthony Saieva, Baishakhi Ray, Gail Kaiser
- How could Neural Networks understand Programs? Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu
- DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons Dawn Drain, Colin B. Clement, Guillermo Serrato, Neel Sundaresan
- Generating Bug-Fixes Using Pretrained Transformers Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan
- Improving Code Autocompletion with Transfer Learning Wen Zhou, Seohyun Kim, Vijayaraghavan Murali, Gareth Ari Aye
- 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
- Contrastive Learning for Source Code with Structural and Functional Properties Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty
- Learning to Extend Program Graphs to Work-in-Progress Code Xuechen Li, Chris J. Maddison, Daniel Tarlow
- Time-Efficient Code Completion Model for the R Programming Language Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev
- What do pre-trained code models know about code? Anjan Karmakar, Romain Robbes
- CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes, Burkhard Rost
- On Multi-Modal Learning of Editing Source Code Saikat Chakraborty, Baishakhi Ray
- CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi
- 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
- Unified Pre-training for Program Understanding and Generation Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
- TreeBERT: A Tree-Based Pre-Trained Model for Programming Language Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, Lei Lyu
- 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
- Code Translation with Compiler Representations Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, Gabriel Synnaeve
- 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
- 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
- A Conversational Paradigm for Program Synthesis Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong
- 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
- Learning to Complete Code with Sketches Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis
- 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
- SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, Bin Luo
- Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes Cedric Richter, Heike Wehrheim
- CodeT: Code Generation with Generated Tests Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
- 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
- Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths
- Using Developer Discussions to Guide Fixing Bugs in Software Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney
- DocCoder: Generating Code by Retrieving and Reading Docs Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao JIang, Graham Neubig
- 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
- Learning to Answer Semantic Queries over Code Surya Prakash Sahu, Madhurima Mandal, Shikhar Bharadwaj, Aditya Kanade, Petros Maniatis, Shirish Shevade
- Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code Patrick Bareiß, Beatriz Souza, Marcelo d'Amorim, Michael Pradel
- Learning code summarization from a small and local dataset Toufique Ahmed, Premkumar Devanbu
- Exploring and Evaluating Personalized Models for Code Generation Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin Clement, Neel Sundaresan, Michele Tufano
- Learning to Reduce False Positives in Analytic Bug Detectors Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan
- Using Deep Learning to Generate Complete Log Statements Antonio Mastropaolo, Luca Pascarella, Gabriele Bavota
- An Extensive Study on Pre-trained Models for Program Understanding and Generation Zhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li, Yuqun Zhang, Lingming Zhang
- 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
- Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation Xin-Ye Li, Jiang-Tian Xue, Zheng Xie, Ming Li
- Improving Few-Shot Prompts with Relevant Static Analysis Products Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, Earl T. Barr
- CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code Shuyan Zhou, Uri Alon, Sumit Agarwal, Graham Neubig
- StarCoder: may the source be with you! Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
- Large Language Models and Simple, Stupid Bugs Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu, Emily Morgan
- TypeT5: Seq2seq Type Inference using Static Analysis Jiayi Wei, Greg Durrett, Isil Dillig
- 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
- Code Execution with Pre-trained Language Models Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan
- CodeT5+: Open Code Large Language Models for Code Understanding and Generation Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D. Q. Bui, Junnan Li, Steven C. H. Hoi
- CodeScore: Evaluating Code Generation by Learning Code Execution Yihong Dong, Jiazheng Ding, Xue Jiang, Zhuo Li, Ge Li, Zhi Jin
- DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection Yizheng Chen, Zhoujie Ding, Xinyun Chen, David Wagner
- T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble Reza Gharibi, Mohammad Hadi Sadreddini, Seyed Mostafa Fakhrahmad
🏷 Transformers
🏷 translation
🏷 types
- Predicting Program Properties from “Big Code” Veselin Raychev, Martin Vechev, Andreas Krause
- Deep Learning Type Inference V. J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis
- RefiNym: Using Names to Refine Types Santanu Dash, Miltiadis Allamanis, Earl T. Barr
- Inferring Javascript types using Graph Neural Networks Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson
- 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
- Learning Lenient Parsing & Typing via Indirect Supervision Toufique Ahmed, Vincent Hellendoorn, Premkumar Devanbu
- OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton
- LambdaNet: Probabilistic Type Inference using Graph Neural Networks Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
- Typilus: Neural Type Hints Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
- Adversarial Robustness for Code Pavol Bielik, Martin Vechev
- Learning Type Annotation: Is Big Data Enough? Kevin Jesse, Premkumar Devanbu, Toufique Ahmed
- ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference Amir M. Mir, Evaldas Latoskinas, Georgios Gousios
- Type4Py: Deep Similarity Learning-Based Type Inference for Python Amir M. Mir, Evaldas Latoskinas, Sebastian Proksch, Georgios Gousios
- Learning To Predict User-Defined Types Kevin Jesse, Premkumar T. Devanbu, Anand Sawant
- LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard
- TypeT5: Seq2seq Type Inference using Static Analysis Jiayi Wei, Greg Durrett, Isil Dillig
- Generative Type Inference for Python Yun Peng, Chaozheng Wang, Wenxuan Wang, Cuiyun Gao, Michael R. Lyu
🏷 variable misuse
- SmartPaste: Learning to Adapt Source Code Miltiadis Allamanis, Marc Brockschmidt
- Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
- Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Anima Anandkumar
- 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
🏷 vulnerability