Awesome Machine Learning On Source Code

Notice: This repository is no longer actively maintained, and no further updates will be done, nor issues/PRs will be answered or attended. An alternative actively maintained can be found at ml4code.github.io repository.

A curated list of awesome research papers, datasets and software projects devoted to machine learning and source code. #MLonCode

Contents

• Digests
• Conferences
• Competitions
• Papers
  • Program Synthesis and Induction
  • Source Code Analysis and Language modeling
  • Neural Network Architectures and Algorithms
  • Embeddings in Software Engineering
  • Program Translation
  • Code Suggestion and Completion
  • Program Repair and Bug Detection
  • APIs and Code Mining
  • Code Optimization
  • Topic Modeling
  • Sentiment Analysis
  • Code Summarization
  • Clone Detection
  • Differentiable Interpreters
  • Related research<details><summary>(links require "Related research" spoiler to be open)</summary>
    • AST Differencing
    • Binary Data Modeling
    • Soft Clustering Using T-mixture Models
    • Natural Language Parsing and Comprehension </details>
• Posts
• Talks
• Software
  • Machine Learning
  • Utilities
• Datasets
• Credits
• Contributions
• License

Digests

• Learning from "Big Code" - Techniques, challenges, tools, datasets on "Big Code".
• A Survey of Machine Learning for Big Code and Naturalness - Survey and literature review on Machine Learning on Source Code.

Conferences

• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> ACM International Conference on Software Engineering, ICSE
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> ACM International Conference on Automated Software Engineering, ASE
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE)
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> 2018 IEEE 25th International Conference on Software Analysis, Evolution, and Reengineering (SANER)
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> Machine Learning for Programming
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> Workshop on NLP for Software Engineering
• <img src="badges/origin-industry-green.svg" alt="origin-industry" align="top"> SysML
  • Talks
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> Mining Software Repositories
• <img src="badges/origin-industry-green.svg" alt="origin-industry" align="top"> AIFORSE
• <img src="badges/origin-industry-green.svg" alt="origin-industry" align="top"> source{d} tech talks
  • Talks
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> NIPS Neural Abstract Machines and Program Induction workshop
  • Talks
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> CamAIML
  • Learning to Code: Machine Learning for Program Induction - Alexander Gaunt.
• <img src="badges/origin-academia-blue.svg" alt="origin-academia" align="top"> MASES 2018

Competitions

• CodRep - competition on automatic program repair: given a source line, find the insertion point.

Papers

Program Synthesis and Induction

• <img src="badges/12-pages-gray.svg" alt="12-pages" align="top"> Program Synthesis and Semantic Parsing with Learned Code Idioms - Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Oleksandr Polozov, 2019.
• <img src="badges/16-pages-gray.svg" alt="16-pages" align="top"> Synthetic Datasets for Neural Program Synthesis - Richard Shin, Neel Kant, Kavi Gupta, Chris Bender, Brandon Trabucco, Rishabh Singh, Dawn Song, ICLR 2019.
• <img src="badges/15-pages-gray.svg" alt="15-pages" align="top"> Execution-Guided Neural Program Synthesis - Xinyun Chen, Chang Liu, Dawn Song, ICLR 2019.
• <img src="badges/8-pages-gray.svg" alt="8-pages" align="top"> DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing - Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu, AAAI 2019.
• <img src="badges/12-pages-beginner-brightgreen.svg" alt="12-pages-beginner" align="top"> 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, LREC 2018.
• <img src="badges/18-pages-gray.svg" alt="18-pages" align="top"> Recent Advances in Neural Program Synthesis - Neel Kant, 2018.
• <img src="badges/16-pages-gray.svg" alt="16-pages" align="top"> Neural Sketch Learning for Conditional Program Generation - Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine, ICLR 2018.
• <img src="badges/11-pages-gray.svg" alt="11-pages" align="top"> Neural Program Search: Solving Programming Tasks from Description and Examples - Illia Polosukhin, Alexander Skidanov, ICLR 2018.
• <img src="badges/16-pages-gray.svg" alt="16-pages" align="top"> Neural Program Synthesis with Priority Queue Training - Daniel A. Abolafia, Mohammad Norouzi, Quoc V. Le, 2018.
• <img src="badges/31-pages-gray.svg" alt="31-pages" align="top"> Towards Synthesizing Complex Programs from Input-Output Examples - Xinyun Chen, Chang Liu, Dawn Song, ICLR 2018.
• <img src="badges/8-pages-gray.svg" alt="8-pages" align="top"> Glass-Box Program Synthesis: A Machine Learning Approach - Konstantina Christakopoulou, Adam Tauman Kalai, AAAI 2018.
• <img src="badges/14-pages-beginner-brightgreen.svg" alt="14-pages" align="top"> Synthesizing Benchmarks for Predictive Modeling - Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather, CGO 2017
• <img src="badges/17-pages-beginner-brightgreen.svg" alt="17-pages-beginner" align="top"> Program Synthesis for Character Level Language Modeling - Pavol Bielik, Veselin Raychev, Martin Vechev, ICLR 2017.
• <img src="badges/13-pages-beginner-brightgreen.svg" alt="13-pages-beginner" align="top"> SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning - Xiaojun Xu, Chang Liu, Dawn Song, 2017.
• <img src="badges/12-pages-gray.svg" alt="12-pages" align="top"> Learning to Select Examples for Program Synthesis - Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Pack Kaelbling, 2017.
• <img src="badges/10-pages-gray.svg" alt="10-pages" align="top"> Neural Program Meta-Induction - Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli, NIPS 2017.
• <img src="badges/14-pages-beginner-brightgreen.svg" alt="14-pages-beginner" align="top"> Learning to Infer Graphics Programs from Hand-Drawn Images - Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Joshua B. Tenenbaum, 2017.
• <img src="badges/10-pages-gray.svg" alt="10-pages" align="top"> Neural Attribute Machines for Program Generation - Matthew Amodio, Swarat Chaudhuri, Thomas Reps, 2017.
• <img src="badges/11-pages-beginner-brightgreen.svg" alt="11-pages-beginner" align="top"> [Abstract Syntax Networks for Code Generation and Semantic Parsing]