# Tag that issue: Applying API-domain labels in issue tracking systems

arXiv: https://arxiv.org/abs/2304.02877 · HTML: https://mlsanity.com/paper/2304.02877/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 79 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [The Impact of Automated Parameter Optimization on Defect Prediction Models](https://mlsanity.com/paper/1801.10270/) (2018)
- [Topic Recommendation for Software Repositories using Multi-label Classification Algorithms](https://mlsanity.com/paper/2010.09116/) (2020)
- [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)
- [A Simple NLP-based Approach to Support Onboarding and Retention in Open Source Communities](https://mlsanity.com/paper/1806.02592/) (2018)
- [Code Smell Detection using Multilabel Classification Approach](https://mlsanity.com/paper/1902.03222/) (2019)
- [A Bug or a Suggestion? An Automatic Way to Label Issues](https://mlsanity.com/paper/1909.00934/) (2019)
- [Bug or not bug? That is the question](https://mlsanity.com/paper/2103.12218/) (2021)

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

- [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)

## Abstract (arXiv)

Labeling issues with the skills required to complete them can help contributors to choose tasks in Open Source Software projects. However, manually labeling issues is time-consuming and error-prone, and current automated approaches are mostly limited to classifying issues as bugs/non-bugs. We investigate the feasibility and relevance of automatically labeling issues with what we call "API-domains," which are high-level categories of APIs. Therefore, we posit that the APIs used in the source code affected by an issue can be a proxy for the type of skills (e.g., DB, security, UI) needed to work on the issue. We ran a user study (n=74) to assess API-domain labels' relevancy to potential contributors, leveraged the issues' descriptions and the project history to build prediction models, and validated the predictions with contributors (n=20) of the projects. Our results show that (i) newcomers to the project consider API-domain labels useful in choosing tasks, (ii) labels can be predicted with a precision of 84% and a recall of 78.6% on average, (iii) the results of the predictions reached up to 71.3% in precision and 52.5% in recall when training with a project and testing in another (transfer learning), and (iv) project contributors consider most of the predictions helpful in identifying needed skills. These findings suggest our approach can be applied in practice to automatically label issues, assisting developers in finding tasks that better match their skills.