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Tag that issue: applying API-domain labels in issue tracking systems
Northern Arizona Unversity, United States.
Northern Arizona Unversity, United States.
Northern Arizona Unversity, United States.
Northern Arizona Unversity, United States.
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2023 (English)In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 28, no 5, article id 116Article in journal (Refereed) Published
Abstract [en]

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. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Place, publisher, year, edition, pages
Springer, 2023. Vol. 28, no 5, article id 116
Keywords [en]
API identification, Labelling, Mining software repositories, Multi-label classification, Skills, Tagging, Classification (of information), Open source software, Open systems, Tracking (position), Issue Tracking, Labelings, Mining software, Mining software repository, Multi-label classifications, Skill, Software repositories, Tracking system, Forecasting
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-25383DOI: 10.1007/s10664-023-10329-4ISI: 001058649500001Scopus ID: 2-s2.0-85169590999OAI: oai:DiVA.org:bth-25383DiVA, id: diva2:1797539
Available from: 2023-09-15 Created: 2023-09-15 Last updated: 2023-09-15Bibliographically approved

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Britto, Ricardo

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