awesome-machine-learning-on-source-code

Source Code ML

A curated list of research papers, datasets, and projects exploring machine learning applications on source code

Cool links & research papers related to Machine Learning applied to source code (MLonCode)

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Awesome Machine Learning On Source Code / 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

Awesome 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

Awesome Machine Learning On Source Code / Conferences / SysML
Talks

Awesome Machine Learning On Source Code / Conferences
Mining Software Repositories
AIFORSE
source{d} tech talks

Awesome Machine Learning On Source Code / Conferences / source{d} tech talks
Talks

Awesome Machine Learning On Source Code / Conferences
NIPS Neural Abstract Machines and Program Induction workshop

Awesome Machine Learning On Source Code / Conferences / NIPS Neural Abstract Machines and Program Induction workshop
Talks

Awesome Machine Learning On Source Code / Conferences
CamAIML

Awesome Machine Learning On Source Code / Conferences / CamAIML
Learning to Code: Machine Learning for Program Induction Alexander Gaunt

Awesome Machine Learning On Source Code / Conferences
MASES 2018

Awesome Machine Learning On Source Code / Competitions
CodRep 91 over 5 years ago competition on automatic program repair: given a source line, find the insertion point

Awesome Machine Learning On Source Code / Papers
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
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.
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.
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
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
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 40 over 6 years ago 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
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
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
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 Singh, ICLR 2019
Compiler Fuzzing through Deep Learning Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather, ISSTA 2018
Automatically assessing vulnerabilities discovered by compositional analysis Saahil Ognawala, Ricardo Nales Amato, Alexander Pretschner and Pooja Kulkarni, MASES 2018
An Empirical Investigation into Learning Bug-Fixing Patches in the Wild via Neural Machine Translation Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk, ASE 2018
DeepBugs: A Learning Approach to Name-based Bug Detection Michael Pradel, Koushik Sen, 2018
Learning How to Mutate Source Code from Bug-Fixes Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk, 2018
A deep tree-based model for software defect prediction HK Dam, T Pham, SW Ng, , J Grundy, A Ghose, T Kim, CJ Kim, 2018
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, 2018
Shaping Program Repair Space with Existing Patches and Similar Code Jiajun Jiang, Yingfei Xiong, Hongyu Zhang, Qing Gao, Xiangqun Chen, 2018. ( )
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks Jacob A. Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin, 2018
Dynamic Neural Program Embedding for Program Repair Ke Wang, Rishabh Singh, Zhendong Su, ICLR 2018
Estimating defectiveness of source code: A predictive model using GitHub content Ritu Kapur, Balwinder Sodhi, 2018
Automated software vulnerability detection with machine learning Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell, Onur Ozdemir, Leonard R. Kosta, Akshay Rangamani, Lei H. Hamilton, Gabriel I. Centeno, Jonathan R. Key, Paul M. Ellingwood, Marc W. McConley, Jeffrey M. Opper, Peter Chin, Tomo Lazovich, IWSPA 2018
Learning a Static Analyzer from Data Pavol Bielik, Veselin Raychev, Martin Vechev, CAV 2017.
To Type or Not to Type: Quantifying Detectable Bugs in JavaScript Zheng Gao, Christian Bird, Earl Barr, ICSE 2017
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities Martin White, Michele Tufano, Matías Martínez, Martin Monperrus, Denys Poshyvanyk, 2017
Semantic Code Repair using Neuro-Symbolic Transformation Networks Jacob Devlin, Jonathan Uesato, Rishabh Singh, Pushmeet Kohli, 2017
Automated Identification of Security Issues from Commit Messages and Bug Reports Yaqin Zhou and Asankhaya Sharma, FSE 2017
SmartPaste: Learning to Adapt Source Code Miltiadis Allamanis, Marc Brockschmidt, 2017
End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks Min-je Choi, Sehun Jeong, Hakjoo Oh, Jaegul Choo, IJCAI 2017
Tailored Mutants Fit Bugs Better Miltiadis Allamanis, Earl T. Barr, René Just, Charles Sutton, 2016
SAR: Learning Cross-Language API Mappings with Little Knowledge Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang, FSE 2019
Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code Nghi D. Q. Bui, Lingxiao Jiang, ICSE 2018
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim, IJCAI 2017
Mining Change Histories for Unknown Systematic Edits Tim Molderez, Reinout Stevens, Coen De Roover, MSR 2017
Deep API Learning Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim, FSE 2016
Exploring API Embedding for API Usages and Applications Nguyen, Nguyen, Phan and Nguyen, Journal of Systems and Software 2017
API usage pattern recommendation for software development Haoran Niu, Iman Keivanloo, Ying Zou, 2017
Parameter-Free Probabilistic API Mining across GitHub Jaroslav Fowkes, Charles Sutton, FSE 2016
A Subsequence Interleaving Model for Sequential Pattern Mining Jaroslav Fowkes, Charles Sutton, KDD 2016
Lean GHTorrent: GitHub data on demand Georgios Gousios, Bogdan Vasilescu, Alexander Serebrenik, Andy Zaidman, MSR 2014
Mining idioms from source code Miltiadis Allamanis, Charles Sutton, FSE 2014
The GHTorent Dataset and Tool Suite Georgios Gousios, MSR 2013
The Case for Learned Index Structures Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis, SIGMOD 2018
End-to-end Deep Learning of Optimization Heuristics Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather, PACT 2017
Learning to superoptimize programs Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H.S. Torr, Pushmeet Kohlim ICLR 2017
Neural Nets Can Learn Function Type Signatures From Binaries Zheng Leong Chua, Shiqi Shen, Prateek Saxena, and Zhenkai Liang, USENIX Security Symposium 2017
Adaptive Neural Compilation Rudy Bunel, Alban Desmaison, Pushmeet Kohli, Philip H.S. Torr, M. Pawan Kumar, NIPS 2016
Learning to Superoptimize Programs - Workshop Version Bunel, Rudy, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, and Pushmeet Kohli, NIPS 2016
A Language-Agnostic Model for Semantic Source Code Labeling Ben Gelman, Bryan Hoyle, Jessica Moore, Joshua Saxe and David Slater, MASES 2018
Topic modeling of public repositories at scale using names in source code Vadim Markovtsev, Eiso Kant, 2017
Why, When, and What: Analyzing Stack Overflow Questions by Topic, Type, and Code Miltiadis Allamanis, Charles Sutton, MSR 2013
Semantic clustering: Identifying topics in source code Adrian Kuhn, Stéphane Ducasse, Tudor Girba, Information & Software Technology 2007
A Benchmark Study on Sentiment Analysis for Software Engineering Research Nicole Novielli, Daniela Girardi, Filippo Lanubile, MSR 2018
Sentiment Analysis for Software Engineering: How Far Can We Go? Bin Lin, Fiorella Zampetti, Gabriele Bavota, Massimiliano Di Penta, Michele Lanza, Rocco Oliveto, ICSE 2018
Leveraging Automated Sentiment Analysis in Software Engineering Md Rakibul Islam, Minhaz F. Zibran, MSR 2017
Sentiment Polarity Detection for Software Development Fabio Calefato, Filippo Lanubile, Federico Maiorano, Nicole Novielli, Empirical Software Engineering 2017
SentiCR: A Customized Sentiment Analysis Tool for Code Review Interactions Toufique Ahmed, Amiangshu Bosu, Anindya Iqbal, Shahram Rahimi, ASE 2017
Summarizing Source Code with Transferred API Knowledge Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, Zhi Jin, IJCAI 2018
Deep Code Comment Generation Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin, ICPC 2018
A Neural Framework for Retrieval and Summarization of Source Code Qingying Chen, Minghui Zhou, ASE 2018
Improving Automatic Source Code Summarization via Deep Reinforcement Learning Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu and Philip S. Yu, ASE 2018
A Convolutional Attention Network for Extreme Summarization of Source Code Miltiadis Allamanis, Hao Peng, Charles Sutton, ICML 2016
TASSAL: Autofolding for Source Code Summarization Jaroslav Fowkes, Pankajan Chanthirasegaran, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton, ICSE 2016
Summarizing Source Code using a Neural Attention Model 236 almost 2 years ago Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer, ACL 2016
Automatic Generation of Pull Request Descriptions Zhongxin Liu, Xin Xia, Christoph Treude, David Lo, Shanping Li, ASE 2019
Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection Lutz Büch and Artur Andrzejak, SANER 2019
Oreo: detection of clones in the twilight zone Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Pierre Baldi, and Cristina V. Lopes, FSE 2018
A Deep Learning Approach to Program Similarity Niccolò Marastoni, Roberto Giacobazzi and Mila Dalla Preda, MASES 2018
Recurrent Neural Network for Code Clone Detection Arseny Zorin and Vladimir Itsykson, SEIM 2018
The Adverse Effects of Code Duplication in Machine Learning Models of Code Miltiadis Allamanis, 2018
DéjàVu: a map of code duplicates on GitHub Cristina V. Lopes, Petr Maj, Pedro Martins, Vaibhav Saini, Di Yang, Jakub Zitny, Hitesh Sajnani, Jan Vitek, Programming Languages OOPSLA 2017
Some from Here, Some from There: Cross-project Code Reuse in GitHub Mohammad Gharehyazie, Baishakhi Ray, Vladimir Filkov, MSR 2017
Deep Learning Code Fragments for Code Clone Detection Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk, ASE 2016
A study of repetitiveness of code changes in software evolution HA Nguyen, AT Nguyen, TT Nguyen, TN Nguyen, H Rajan, ASE 2013
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer Joseph Suarez, Justin Johnson, Fei-Fei Li, 2018
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction Da Xiao, Jo-Yu Liao, Xingyuan Yuan, ICLR 2018
Differentiable Programs with Neural Libraries Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow, ICML 2017
Differentiable Functional Program Interpreters John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow, 2017
Programming with a Differentiable Forth Interpreter Bošnjak, Matko, Tim Rocktäschel, Jason Naradowsky, and Sebastian Riedel, ICML 2017
Neural Functional Programming Feser John K., Marc Brockschmidt, Alexander L. Gaunt, and Daniel Tarlow, ICLR 2017
TerpreT: A Probabilistic Programming Language for Program Induction Gaunt, Alexander L., Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, and Daniel Tarlow, NIPS 2016
ClDiff: Generating Concise Linked Code Differences Kaifeng Huang, Bihuan Chen, Xin Peng, Daihong Zhou, Ying Wang, Yang Liu, Wenyun Zhao, ASE 2018.
Generating Accurate and Compact Edit Scripts Using Tree Differencing Veit Frick, Thomas Grassauer, Fabian Beck, Martin Pinzger, ICSME 2018
Fine-grained and Accurate Source Code Differencing Jean-Rémy Falleri, Floréal Morandat, Xavier Blanc, Matias Martinez, Martin Monperrus, ASE 2014
Clustering Binary Data with Bernoulli Mixture Models Neal S. Grantham
A Family of Blockwise One-Factor Distributions for Modelling High-Dimensional Binary Data Matthieu Marbac and Mohammed Sedki, Computational Statistics & Data Analysis 2017
BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data Panagiotis Papastamoulis and Magnus Rattray, R Journal 2016
Robust mixture modelling using the t distribution D. Peel and G. J. McLachlan, Statistics and Computing 2000
Robust mixture modeling using the skew t distribution Tsung I. Lin, Jack C. Lee and Wan J. Hsieh, Statistics and Computing 2010
A Fast Unified Model for Parsing and Sentence Understanding Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, Christopher Potts, ACL 2016

Awesome Machine Learning On Source Code / Posts
Semantic Code Search
Learning from Source Code
Training a Model to Summarize Github Issues
Sequence Intent Classification Using Hierarchical Attention Networks
Syntax-Directed Variational Autoencoder for Structured Data
Weighted MinHash on GPU helps to find duplicate GitHub repositories.
Source Code Identifier Embeddings
Using recurrent neural networks to predict next tokens in the java solutions
The half-life of code & the ship of Theseus
The eigenvector of "Why we moved from language X to language Y"
Analyzing Github, How Developers Change Programming Languages Over Time
Topic Modeling of GitHub Repositories
Aroma: Using machine learning for code recommendation

Awesome Machine Learning On Source Code / Talks
Machine Learning on Source Code
Similarity of GitHub Repositories by Source Code Identifiers
Using deep RNN to model source code
Source code abstracts classification using CNN (1)
Source code abstracts classification using CNN (2)
Source code abstracts classification using CNN (3)
Embedding the GitHub contribution graph
Measuring code sentiment in a Git repository

Awesome Machine Learning On Source Code / Software
Differentiable Neural Computer (DNC) 2,501 over 3 years ago TensorFlow implementation of the Differentiable Neural Computer
sourced.ml 141 over 5 years ago Abstracts feature extraction from source code syntax trees and working with ML models
vecino 46 over 5 years ago Finds similar Git repositories
apollo 52 about 2 years ago Source code deduplication as scale, research
gemini 54 over 5 years ago Source code deduplication as scale, production
enry 460 about 3 years ago Insanely fast file based programming language detector
hercules 2,633 almost 2 years ago Git repository mining framework with batteries on top of go-git
DeepCS 279 over 2 years ago Keras and Pytorch implementations of DeepCS (Deep Code Search)
Code Neuron 13 almost 6 years ago Recurrent neural network to detect code blocks in natural language text
Naturalize 56 about 9 years ago Language agnostic framework for learning coding conventions from a codebase and then expoiting this information for suggesting better identifier names and formatting changes in the code
Extreme Source Code Summarization 119 over 8 years ago Convolutional attention neural network that learns to summarize source code into a short method name-like summary by just looking at the source code tokens
Summarizing Source Code using a Neural Attention Model 236 almost 2 years ago CODE-NN, uses LSTM networks with attention to produce sentences that describe C# code snippets and SQL queries from StackOverflow. Torch over C#/SQL
Probabilistic API Miner 53 almost 7 years ago Near parameter-free probabilistic algorithm for mining the most interesting API patterns from a list of API call sequences
Interesting Sequence Miner 44 over 6 years ago Novel algorithm that mines the most interesting sequences under a probabilistic model. It is able to efficiently infer interesting sequences directly from the database
TASSAL 42 over 8 years ago Tool for the automatic summarization of source code using autofolding. Autofolding automatically creates a summary of a source code file by folding non-essential code and comment blocks
JNice2Predict Efficient and scalable open-source framework for structured prediction, enabling one to build new statistical engines more quickly
Clone Digger clone detection for Python and Java
Sensibility 18 about 3 years ago Uses LSTMs to detect and correct syntax errors in Java source code
DeepBugs 148 over 3 years ago Framework for learning bug detectors from an existing code corpus
DeepSim 60 over 5 years ago a deep learning-based approach to measure code functional similarity
rnn-autocomplete 9 over 5 years ago Neural code autocompletion with RNN (bachelor's thesis)
MindsDB 26,830 about 20 hours ago MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code
go-git 4,897 over 2 years ago Highly extensible Git implementation in pure Go which is friendly to data mining
bblfsh Self-hosted server for source code parsing
engine 188 about 5 years ago Scalable and distributed data retrieval pipeline for source code
minhashcuda 114 12 months ago Weighted MinHash implementation on CUDA to efficiently find duplicates
kmcuda 806 about 2 years ago k-means on CUDA to cluster and to search for nearest neighbors in dense space
wmd-relax 460 over 1 year ago Python package which finds nearest neighbors at Word Mover's Distance
Tregex, Tsurgeon and Semgrex Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for "tree regular expressions")
source{d} models 19 about 5 years ago Machine Learning models for MLonCode trained using the source{d} stack
Neural-Code-Search-Evaluation-Dataset 123 7 months ago dataset contains links to 4.7M methods from 24k+ repositories with 287 StackOverflow questions and code snippet answers
CodeSearchNet 2,213 almost 3 years ago collection of datasets and benchmarks for code retrieval using natural language. Contains 2M pairs of ( , )
Public Git Archive 323 almost 5 years ago 6 TB of Git repositories from GitHub
StackOverflow Question-Code Dataset 165 about 3 years ago ~148K Python and ~120K SQL question-code pairs mined from StackOverflow
GitHub Issue Titles and Descriptions for NLP Analysis ~8 million GitHub issue titles and descriptions from 2017
GitHub repositories - languages distribution Programming languages distribution in 14,000,000 repositories on GitHub (October 2016)
452M commits on GitHub ≈ 452M commits' metadata from 16M repositories on GitHub (October 2016)
GitHub readme files Readme files of all GitHub repositories (16M) (October 2016)
from language X to Y Cache file Erik Bernhardsson collected for his awesome blog post
GitHub word2vec 120k Sequences of identifiers extracted from top starred 120,000 GitHub repositories
GitHub Source Code Names Names in source code extracted from 13M GitHub repositories, not people
GitHub duplicate repositories GitHub repositories not marked as forks but very similar to each other
GitHub lng keyword frequencies Programming language keyword frequency extracted from 16M GitHub repositories
GitHub Java Corpus GitHub Java corpus is a set of Java projects collected from GitHub that we have used in a number of our publications. The corpus consists of 14,785 projects and 352,312,696 LOC
150k Python Dataset Dataset consisting of 150,000 Python ASTs
150k JavaScript Dataset Dataset consisting of 150,000 JavaScript files and their parsed ASTs
card2code 243 almost 7 years ago This dataset contains the language to code datasets described in the paper
NL2Bash 451 3 months ago This dataset contains a set of ~10,000 bash one-liners collected from websites such as StackOverflow and their English descriptions written by Bash programmers, as described in the
GitHub JavaScript Dump October 2016 Dataset consisting of 494,352 syntactically-valid JavaScript files obtained from the top ~10000 starred JavaScript repositories on GitHub, with licenses, and parsed ASTs
BigCloneBench Clone detection benchmark of 8 million function clone pairs in the IJaDataset

Awesome Machine Learning On Source Code / Credits
mast-group A lot of references and articles were taken from
Awesome Machine Learning 66,046 12 days ago Inspired by

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