# GiveMeLabeledIssues: An Open Source Issue Recommendation System

arXiv: https://arxiv.org/abs/2303.13418 · HTML: https://mlsanity.com/paper/2303.13418/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 23 Mar 2023.

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

- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [Predicting the Objective and Priority of Issue Reports in Software Repositories](https://mlsanity.com/paper/2012.10951/) (2020)
- [Can I Solve It? Identifying APIs Required to Complete OSS Task](https://mlsanity.com/paper/2103.12653/) (2021)
- [How Do Software Developers Use GitHub Actions to Automate Their Workflows?](https://mlsanity.com/paper/2103.12224/) (2021)
- [A Bug or a Suggestion? An Automatic Way to Label Issues](https://mlsanity.com/paper/1909.00934/) (2019)
- [Representation of Developer Expertise in Open Source Software](https://mlsanity.com/paper/2005.10176/) (2020)
- [Bug or not bug? That is the question](https://mlsanity.com/paper/2103.12218/) (2021)
- [Identifying Experts in Software Libraries and Frameworks among GitHub Users](https://mlsanity.com/paper/1903.08113/) (2019)

## Built on by (most cited first, 3 of 3)

- [SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub](https://mlsanity.com/paper/2501.15922/) (2025)
- [Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models](https://mlsanity.com/paper/2506.00128/) (2025)
- [Applying Large Language Models API to Issue Classification Problem](https://mlsanity.com/paper/2401.04637/) (2024)

## Abstract (arXiv)

Developers often struggle to navigate an Open Source Software (OSS) project's issue-tracking system and find a suitable task. Proper issue labeling can aid task selection, but current tools are limited to classifying the issues according to their type (e.g., bug, question, good first issue, feature, etc.). In contrast, this paper presents a tool (GiveMeLabeledIssues) that mines project repositories and labels issues based on the skills required to solve them. We leverage the domain of the APIs involved in the solution (e.g., User Interface (UI), Test, Databases (DB), etc.) as a proxy for the required skills. GiveMeLabeledIssues facilitates matching developers' skills to tasks, reducing the burden on project maintainers. The tool obtained a precision of 83.9% when predicting the API domains involved in the issues. The replication package contains instructions on executing the tool and including new projects. A demo video is available at https://www.youtube.com/watch?v=ic2quUue7i8