# A Versatile Dataset of Agile Open Source Software Projects

arXiv: https://arxiv.org/abs/2202.00979 · HTML: https://mlsanity.com/paper/2202.00979/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [Search-based Optimisation of LLM Learning Shots for Story Point Estimation](https://mlsanity.com/paper/2403.08430/) (2024)
- [Heterogeneous Graph Neural Networks for Software Effort Estimation](https://mlsanity.com/paper/2206.11023/) (2022)
- [User Story Tutor (UST) to Support Agile Software Developers](https://mlsanity.com/paper/2406.16259/) (2024)
- [Recent Advances in Software Effort Estimation using Machine Learning](https://mlsanity.com/paper/2303.03482/) (2023)

## Abstract (arXiv)

Agile software development is nowadays a widely adopted practise in both open-source and industrial software projects. Agile teams typically heavily rely on issue management tools to document new issues and keep track of outstanding ones, in addition to storing their technical details, effort estimates, assignment to developers, and more. Previous work utilised the historical information stored in issue management systems for various purposes; however, when researchers make their empirical data public, it is usually relevant solely to the study's objective. In this paper, we present a more holistic and versatile dataset containing a wealth of information on more than 500,000 issues from 44 open-source Agile software, making it well-suited to several research avenues, and cross-analyses therein, including effort estimation, issue prioritization, issue assignment and many more. We make this data publicly available on GitHub to facilitate ease of use, maintenance, and extensibility.