# LabelGit: A Dataset for Software Repositories Classification using Attributed Dependency Graphs

arXiv: https://arxiv.org/abs/2103.08890 · HTML: https://mlsanity.com/paper/2103.08890/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Topic Recommendation for Software Repositories using Multi-label Classification Algorithms](https://mlsanity.com/paper/2010.09116/) (2020)
- [HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories](https://mlsanity.com/paper/1910.07115/) (2019)

## Abstract (arXiv)

Software repository hosting services contain large amounts of open-source software, with GitHub hosting more than 100 million repositories, from new to established ones. Given this vast amount of projects, there is a pressing need for a search based on the software's content and features. However, even though GitHub offers various solutions to aid software discovery, most repositories do not have any labels, reducing the utility of search and topic-based analysis. Moreover, classifying software modules is also getting more importance given the increase in Component-Based Software Development. However, previous work focused on software classification using keyword-based approaches or proxies for the project (e.g., README), which is not always available. In this work, we create a new annotated dataset of GitHub Java projects called LabelGit. Our dataset uses direct information from the source code, like the dependency graph and source code neural representations from the identifiers. Using this dataset, we hope to aid the development of solutions that do not rely on proxies but use the entire source code to perform classification.