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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Notice: This repository is no longer actively maintained, and no further updates will be done, nor issues/PRs will be answered or attended.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
    (links require "Related research" spoiler to be open)

    • AST Differencing
    • Binary Data Modeling
    • Soft Clustering Using T-mixture Models
    • Natural Language Parsing and Comprehension

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

• ACM International Conference on Software Engineering, ICSE
• ACM International Conference on Automated Software Engineering, ASE
• ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE)
• 2018 IEEE 25th International Conference on Software Analysis, Evolution, and Reengineering (SANER)
• Machine Learning for Programming
• Workshop on NLP for Software Engineering
• SysML
  • Talks
• Mining Software Repositories
• AIFORSE
• source{d} tech talks
  • Talks
• NIPS Neural Abstract Machines and Program Induction workshop
  • Talks
• CamAIML
  • Learning to Code: Machine Learning for Program Induction - Alexander Gaunt.
• MASES 2018

Competitions

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

Papers

Program Synthesis and Induction

• Program Synthesis and Semantic Parsing with Learned Code Idioms - Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Oleksandr Polozov, 2019.
• Synthetic Datasets for Neural Program Synthesis - Richard Shin, Neel Kant, Kavi Gupta, Chris Bender, Brandon Trabucco, Rishabh Singh, Dawn Song, ICLR 2019.
• Execution-Guided Neural Program Synthesis - Xinyun Chen, Chang Liu, Dawn Song, ICLR 2019.
• DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing - Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu, AAAI 2019.
• 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.
• Recent Advances in Neural Program Synthesis - Neel Kant, 2018.
• Neural Sketch Learning for Conditional Program Generation - Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine, ICLR 2018.
• Neural Program Search: Solving Programming Tasks from Description and Examples - Illia Polosukhin, Alexander Skidanov, ICLR 2018.
• Neural Program Synthesis with Priority Queue Training - Daniel A. Abolafia, Mohammad Norouzi, Quoc V. Le, 2018.
• Towards Synthesizing Complex Programs from Input-Output Examples - Xinyun Chen, Chang Liu, Dawn Song, ICLR 2018.
• Glass-Box Program Synthesis: A Machine Learning Approach - Konstantina Christakopoulou, Adam Tauman Kalai, AAAI 2018.
• Synthesizing Benchmarks for Predictive Modeling - Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather, CGO 2017
• Program Synthesis for Character Level Language Modeling - Pavol Bielik, Veselin Raychev, Martin Vechev, ICLR 2017.
• SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning - Xiaojun Xu, Chang Liu, Dawn Song, 2017.
• Learning to Select Examples for Program Synthesis - Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Pack Kaelbling, 2017.
• Neural Program Meta-Induction - Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli, NIPS 2017.
• Learning to Infer Graphics Programs from Hand-Drawn Images - Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Joshua B. Tenenbaum, 2017.
• Neural Attribute Machines for Program Generation - Matthew Amodio, Swarat Chaudhuri, Thomas Reps, 2017.
• Abstract Syntax Networks for Code Generation and Semantic Parsing - Maxim Rabinovich, Mitchell Stern, Dan Klein, ACL 2017.
• Making Neural Programming Architectures Generalize via Recursion - Jonathon Cai, Richard Shin, Dawn Song, ICLR 2017.
• A Syntactic Neural Model for General-Purpose Code Generation - Pengcheng Yin, Graham Neubig, ACL 2017.
• Program Synthesis from Natural Language Using Recurrent Neural Networks - Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, Luke Zettlemoyer, Michael Ernst, 2017.
• RobustFill: Neural Program Learning under Noisy I/O - Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli, ICML 2017.
• Lifelong Perceptual Programming By Example - Gaunt, Alexander L., Marc Brockschmidt, Nate Kushman, and Daniel Tarlow, 2017.
• Neural Programming by Example - Chengxun Shu, Hongyu Zhang, AAAI 2017.
• DeepCoder: Learning to Write Programs - Balog Matej, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow, ICLR 2017.
• A Differentiable Approach to Inductive Logic Programming - Yang Fan, Zhilin Yang, and William W. Cohen, 2017.
• Latent Attention For If-Then Program Synthesis - Xinyun Chen, Chang Liu, Richard Shin, Dawn Song, Mingcheng Chen, NIPS 2016.
• Latent Predictor Networks for Code Generation - Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom, ACL 2016.
• Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version) - Liang Chen, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao, NIPS 2016.
• Programs as Black-Box Explanations - Singh, Sameer, Marco Tulio Ribeiro, and Carlos Guestrin, NIPS 2016.
• Search-Based Generalization and Refinement of Code Templates - Tim Molderez, Coen De Roover, SSBSE 2016.
• Structured Generative Models of Natural Source Code - Chris J. Maddison, Daniel Tarlow, ICML 2014.

Source Code Analysis and Language modeling

• Modeling Vocabulary for Big Code Machine Learning - Hlib Babii, Andrea Janes, Romain Robbes, 2019.
• Generative Code Modeling with Graphs - Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov, ICLR 2019.
• NL2Type: Inferring JavaScript Function Types from Natural Language Information - Rabee Sohail Malik, Jibesh Patra, Michael Pradel, ICSE 2019.
• A Novel Neural Source Code Representation based on Abstract Syntax Tree - Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, Xudong Liu, ICSE 2019.
• Deep Learning Type Inference - Vincent J. Hellendoorn, Christian Bird, Earl T. Barr and Miltiadis Allamanis, FSE 2018. Code.
• Tree2Tree Neural Translation Model for Learning Source Code Changes - Saikat Chakraborty, Miltiadis Allamanis, Baishakhi Ray, 2018.
• code2seq: Generating Sequences from Structured Representations of Code - Uri Alon, Omer Levy, Eran Yahav, 2018.
• Syntax and Sensibility: Using language models to detect and correct syntax errors - Eddie Antonio Santos, Joshua Charles Campbell, Dhvani Patel, Abram Hindle, and José Nelson Amaral, SANER 2018.
• code2vec: Learning Distributed Representations of Code - Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav, 2018.
• Learning to Represent Programs with Graphs - Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi, ICLR 2018.
• A Survey of Machine Learning for Big Code and Naturalness - Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton, 2017.
• Are Deep Neural Networks the Best Choice for Modeling Source Code? - Vincent J. Hellendoorn, Premkumar Devanbu, FSE 2017.
• A deep language model for software code - Hoa Khanh Dam, Truyen Tran, Trang Pham, 2016.
• Convolutional Neural Networks over Tree Structures for Programming Language Processing - Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin, AAAI-16. Code.
• Suggesting Accurate Method and Class Names - Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton, FSE 2015.
• Mining Source Code Repositories at Massive Scale using Language Modeling - Miltiadis Allamanis, Charles Sutton, MSR 2013.

Neural Network Architectures and Algorithms

• Learning Compositional Neural Programs with Recursive Tree Search and Planning - Thomas Pierrot, Guillaume Ligner, Scott Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas, 2019.
• From Programs to Interpretable Deep Models and Back - Eran Yahav, ICCAV 2018.
• Neural Code Comprehension: A Learnable Representation of Code Semantics - Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler, NIPS 2018.
• A General Path-Based Representation for Predicting Program Properties - Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav, PLDI 2018.
• Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks - Nghi D. Q. Bui, Lingxiao Jiang, Yijun Yu, AAAI 2018.
• Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification - Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang, SANER 2018.
• Syntax-Directed Variational Autoencoder for Structured Data - Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song, ICLR 2018.
• Divide and Conquer with Neural Networks - Nowak, Alex, and Joan Bruna, ICLR 2018.
• Hierarchical multiscale recurrent neural networks - Chung Junyoung, Sungjin Ahn, and Yoshua Bengio, ICLR 2017.
• Learning Efficient Algorithms with Hierarchical Attentive Memory - Andrychowicz, Marcin, and Karol Kurach, 2016.
• Learning Operations on a Stack with Neural Turing Machines - Deleu, Tristan, and Joseph Dureau, NIPS 2016.
• Probabilistic Neural Programs - Murray, Kenton W., and Jayant Krishnamurthy, NIPS 2016.
• Neural Programmer-Interpreters - Reed, Scott, and Nando de Freitas, ICLR 2016.
• Neural GPUs Learn Algorithms - Kaiser, Łukasz, and Ilya Sutskever, ICLR 2016.
• Neural Random-Access Machines - Karol Kurach, Marcin Andrychowicz, Ilya Sutskever, ERCIM News 2016.
• Neural Programmer: Inducing Latent Programs with Gradient Descent - Neelakantan, Arvind, Quoc V. Le, and Ilya Sutskever, ICLR 2015.
• Learning to Execute - Wojciech Zaremba, Ilya Sutskever, 2015.
• Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets - Joulin, Armand, and Tomas Mikolov, NIPS 2015.
• Neural Turing Machines - Graves, Alex, Greg Wayne, and Ivo Danihelka, 2014.
• From Machine Learning to Machine Reasoning - Bottou Leon, Journal of Machine Learning 2011.

Embeddings in Software Engineering

• A Literature Study of Embeddings on Source Code - Zimin Chen and Martin Monperrus, 2019.
• AST-Based Deep Learning for Detecting Malicious PowerShell - Gili Rusak, Abdullah Al-Dujaili, Una-May O'Reilly, 2018.
• Deep Code Search - Xiaodong Gu, Hongyu Zhang, Sunghun Kim, ICSE 2018.
• Word Embeddings for the Software Engineering Domain - Vasiliki Efstathiou, Christos Chatzilenas, Diomidis Spinellis, MSR 2018.
• Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces - Jordan Henkel, Shuvendu K. Lahiri, Ben Liblit, Thomas Reps, FSE 2018.
• Document Distance Estimation via Code Graph Embedding - Zeqi Lin, Junfeng Zhao, Yanzhen Zou, Bing Xie, Internetware 2017.
• Combining Word2Vec with revised vector space model for better code retrieval - Thanh Van Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Trong Duc Nguyen, Tien N. Nguyen, ICSE 2017.
• From word embeddings to document similarities for improved information retrieval in software engineering - Xin Ye, Hui Shen, Xiao Ma, Razvan Bunescu, Chang Liu, ICSE 2016.
• Mapping API Elements for Code Migration with Vector Representation - Trong Duc Nguyen, Anh Tuan Nguyen, Tien N. Nguyen, ICSE 2016.

Program Translation

• Towards Neural Decompilation - Omer Katz, Yuval Olshaker, Yoav Goldberg, Eran Yahav, 2019.
• Tree-to-tree Neural Networks for Program Translation - Xinyun Chen, Chang Liu, Dawn Song, ICLR 2018.
• Code Attention: Translating Code to Comments by Exploiting Domain Features - Wenhao Zheng, Hong-Yu Zhou, Ming Li, Jianxin Wu, 2017.
• Automatically Generating Commit Messages from Diffs using Neural Machine Translation - Siyuan Jiang, Ameer Armaly, Collin McMillan, ASE 2017.
• A Parallel Corpus of Python Functions and Documentation Strings for Automated Code Documentation and Code Generation - Antonio Valerio Miceli Barone, Rico Sennrich, ICNLP 2017.
• A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes - Pablo Loyola, Edison Marrese-Taylor, Yutaka Matsuo, ACL 2017.

Code Suggestion and Completion

• Aroma: Code Recommendation via Structural Code Search - Sifei Luan, Di Yang, Koushik Sen and Satish Chandra, 2019.
• Intelligent Code Reviews Using Deep Learning - Anshul Gupta, Neel Sundaresan, KDD DL Day 2018.
• Code Completion with Neural Attention and Pointer Networks - Jian Li, Yue Wang, Irwin King, Michael R. Lyu, 2017.
• Learning Python Code Suggestion with a Sparse Pointer Network - Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr, Sebastian Riedel, 2016.
• Code Completion with Statistical Language Models - Veselin Raychev, Martin Vechev, Eran Yahav, PLDI 2014.

Program Repair and Bug Detection

• SampleFix: Learning to Correct Programs by Sampling Diverse Fixes - Hossein Hajipour, Apratim Bhattacharya, Mario Fritz, 2019.
• Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection - Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel, Lizhen Qu, ICLR 2019.
• Neural Program Repair by Jointly Learning to Localize and Repair - Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh S