A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering
Website for "A Survey of Machine Learning for Big Code and Naturalness"
Implementation of the paper "Language-agnostic representation learning of source code from structure and context".
Repository for the code of the "A Convolutional Attention Network for Extreme Summarization of Source Code" paper
A Tool for Mining Rich Abstract Syntax Trees from Code
PyTorch's implementation of the code2seq model.
The official repository of "GraphSPD: Graph-Based Security Patch Detection with Enriched Code Semantics". The paper will appear in the IEEE Symposium on Security and Privacy (S&P), San Francisco, CA, May 22-26, 2023.
Tree-based Autofolding Software Summarization Algorithm
[ICLR 2021] "Generating Adversarial Computer Programs using Optimized Obfuscations" by Shashank Srikant, Sijia Liu, Tamara Mitrovska, Shiyu Chang, Quanfu Fan, Gaoyuan Zhang, and Una-May O'Reilly
Set of PyTorch modules for developing and evaluating different algorithms for embedding trees.
ComPy-Learn is a framework for exploring program representations for ML4CODE tasks.
Fixes Java syntax errors with LSTM neural networks! [proof-of-concept]
Implementation of 'A Convolutional Attention Network for Extreme Summarization of Source Code' in PyTorch using TorchText
Code and data for "Impact of Evaluation Methodologies on Code Summarization" in ACL 2022.
VSCode Extension of Type4Py
A graph based bug classifier using the dgl library and DeepBugs dataset
Neural Data-Flow Analysis: A tool for solving program-related tasks which involve data-flow analysis using deep neural networks
Extracts code2seq compatible datasets from PHP source files.
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