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Developers often struggle to identify the skills required to work on open issues in Open Source Software (OSS) projects. Proper issue labeling can help task selection, but current strategies 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 that mines project repositories and labels issues based on the skills required to solve them, more specifically the domain of the APIs involved in the solution (e.g., User Interface (UI), Test, Databases (DB), etc.). GiveMeLabeledIssues facilitates matching developers' skills and tasks, reducing the burden on project maintainers by minimizing the amount of manual labeling needed to annotate project issues effectively. The demo toll obtained a precision of 83.9% predicting projects with TF-IDF and Random Forest (RF).
Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Machine Learning, Open Source Software, Machine Learning, Label, Tag, Task, Issue Tracker, Machine Learning (cs.LG)
Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Machine Learning, Open Source Software, Machine Learning, Label, Tag, Task, Issue Tracker, Machine Learning (cs.LG)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 6 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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