A command line tool to mine and analyze data from version-control systems

💾 🧮 🤯 Parse the 'git log' of multiple repos to 'JSON'

HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories (ICDM'19)

With Getaviz you can solve software engineering problems visually by exploring software artifacts them in 2D, 3D, and virtual reality.

Susie checks GitHub repositories for sustainability and provides interesting knowledge for developers regarding sustainable software development.

An agile tool to analyze Git repositories

Github repo to Neo4j (and back)

Python script that scrapes GitHub repositories to keep track of total clone counts. This is useful for projects that are NSF funded, where "impact" (total downloads) is required to be reported.

📊 👁 🧘 Visualise your git log in new and exciting ways!

RelationalGit extracts valuable information about commits, blame, changes, devs, and pull requests out of git's data structure and imports them to a relational database such as Microsoft SQL Server. These data can be a ground for further source code mining analysis.

SAP Security Research sample code and data to reproduce the research done in our paper "Detecting Backdoors in Collaboration Graphs of Software Repositories".

Analyze the Git history of your PHP files

Library for Variability-Aware Differencing and the Analysis of Edits to Preprocessor-Based Software Product Lines

Dive deep into the history of any Git repository. This Python framework provides comprehensive tools for analyzing source code changes, commit metadata, and developer contributions at a granular level.

⛏️ Repository containing scripts and other resources for finding out how many projects use TDD in practice, based on Git history.

Ximdex Semantic Information Repository

Repository mining for searching Performance Antipatterns in Cyber-Physical System Projects.

Code for searching for institutional dataset records from popular data repositories (e.g., Zenodo)

RepoCapsule is a Python toolkit for turning GitHub, local, and other text/code sources into clean JSONL corpora for LLM pre-training, fine-tuning, or RAG. It provides structure-aware chunking, robust Unicode decoding, pluggable quality/safety screening, and optional dataset card + deduplication support.