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.

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

CodRep

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

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. 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.

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

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, T Tran, 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. (code).

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. video.

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

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. Code.

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.

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

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

Differentiable Neural Computer (DNC)

TensorFlow implementation of the Differentiable Neural Computer.

sourced.ml

Abstracts feature extraction from source code syntax trees and working with ML models.

vecino

Finds similar Git repositories.

apollo

Source code deduplication as scale, research.

gemini

Source code deduplication as scale, production.

enry

Insanely fast file based programming language detector.

hercules

Git repository mining framework with batteries on top of go-git.

DeepCS

Keras and Pytorch implementations of DeepCS (Deep Code Search).

Code Neuron

Recurrent neural network to detect code blocks in natural language text.

Naturalize

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

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

Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer, ACL 2016.

Probabilistic API Miner

Near parameter-free probabilistic algorithm for mining the most interesting API patterns from a list of API call sequences.

Interesting Sequence Miner

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

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

Uses LSTMs to detect and correct syntax errors in Java source code.

DeepBugs

Framework for learning bug detectors from an existing code corpus.

DeepSim

a deep learning-based approach to measure code functional similarity.

rnn-autocomplete

Neural code autocompletion with RNN (bachelor's thesis).

MindsDB

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

Highly extensible Git implementation in pure Go which is friendly to data mining.

bblfsh

Self-hosted server for source code parsing.

engine

Scalable and distributed data retrieval pipeline for source code.

minhashcuda

Weighted MinHash implementation on CUDA to efficiently find duplicates.

kmcuda

k-means on CUDA to cluster and to search for nearest neighbors in dense space.

wmd-relax

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

Machine Learning models for MLonCode trained using the source{d} stack.

Neural-Code-Search-Evaluation-Dataset

dataset contains links to 4.7M methods from 24k+ repositories with 287 StackOverflow questions and code snippet answers.

CodeSearchNet

collection of datasets and benchmarks for code retrieval using natural language. Contains 2M pairs of (comment, code).

Public Git Archive

6 TB of Git repositories from GitHub.

StackOverflow Question-Code Dataset

~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

This dataset contains the language to code datasets described in the paper Latent Predictor Networks for Code Generation.

NL2Bash

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 paper.

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.