A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering

Type4Py: Deep Similarity Learning-Based Type Inference for Python

PyTorch's implementation of the code2seq model.

A Tool for Mining Rich Abstract Syntax Trees from Code

TeCo: an ML+Execution model for test completion

[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.

CD4Py: Code De-Duplication for Python

Machine Learning for Source Code Analysis

Pre-training and fine-tuning GNN model on source code

VSCode Extension of Type4Py

This paper explores the idea of using heterogeneous graph neural networks (Het-GNN) to partition old legacy monoliths into candidate microservices. We additionally take membership constraints that come from a subject matter expert who has deep domain knowledge of the application.

On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study

Implementing idea from the "Extracting Training Data from Large Language Models" paper on starcoderbase-3b model