# Automatically Categorising GitHub Repositories by Application Domain

arXiv: https://arxiv.org/abs/2208.00269 · HTML: https://mlsanity.com/paper/2208.00269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 30 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Understanding the Factors that Impact the Popularity of GitHub Repositories](https://mlsanity.com/paper/1606.04984/) (2016)
- [HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories](https://mlsanity.com/paper/1910.07115/) (2019)
- [Categorizing the Content of GitHub README Files](https://mlsanity.com/paper/1802.06997/) (2018)
- [Why Modern Open Source Projects Fail](https://mlsanity.com/paper/1707.02327/) (2017)
- [How Do Software Developers Use GitHub Actions to Automate Their Workflows?](https://mlsanity.com/paper/2103.12224/) (2021)
- [GitHub Discussions: An Exploratory Study of Early Adoption](https://mlsanity.com/paper/2102.05230/) (2021)
- [Newcomer OSS-Candidates: Characterizing Contributions of Novice Developers to GitHub](https://mlsanity.com/paper/2101.08903/) (2021)

## Built on by (most cited first, 2 of 2)

- [Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report](https://mlsanity.com/paper/2608.11965/) (2026)
- [Industry Classification of GitHub Repositories Using the North American Industry Classification System (NAICS)](https://mlsanity.com/paper/2607.06505/) (2026)

## Abstract (arXiv)

GitHub is the largest host of open source software on the Internet. This large, freely accessible database has attracted the attention of practitioners and researchers alike. But as GitHub's growth continues, it is becoming increasingly hard to navigate the plethora of repositories which span a wide range of domains. Past work has shown that taking the application domain into account is crucial for tasks such as predicting the popularity of a repository and reasoning about project quality. In this work, we build on a previously annotated dataset of 5,000 GitHub repositories to design an automated classifier for categorising repositories by their application domain. The classifier uses state-of-the-art natural language processing techniques and machine learning to learn from multiple data sources and catalogue repositories according to five application domains. We contribute with (1) an automated classifier that can assign popular repositories to each application domain with at least 70% precision, (2) an investigation of the approach's performance on less popular repositories, and (3) a practical application of this approach to answer how the adoption of software engineering practices differs across application domains. Our work aims to help the GitHub community identify repositories of interest and opens promising avenues for future work investigating differences between repositories from different application domains.