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

• URL: http://arxiv.org/abs/2304.02877v1
• Date: Thu, 6 Apr 2023 05:49:46 GMT
• Title: Tag that issue: Applying API-domain labels in issue tracking systems
• Authors: Fabio Santos, Joseph Vargovich, Bianca Trinkenreich, Italo Santos, Jacob Penney, Ricardo Britto, Jo\~ao Felipe Pimentel, Igor Wiese, Igor Steinmacher, Anita Sarma, Marco A. Gerosa
• Abstract summary: Labeling issues with the skills required to complete them can help contributors to choose tasks in Open Source Software projects. We investigate the feasibility and relevance of automatically labeling issues with what we call "API-domains," which are high-level categories of APIs. Our results show that newcomers 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, and (iv) project
• Score: 20.701637107734996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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