GiveMeLabeledIssues: An Open Source Issue Recommendation System

Joseph Vargovich, Fabio Santos, Jacob Penney, Marco A. Gerosa, Igor Steinmacher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/ACM 20th International Conference on Mining Software Repositories, MSR 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages402-406
Number of pages5
ISBN (Electronic)9798350311846
DOIs
StatePublished - 2023
Externally publishedYes
Event20th IEEE/ACM International Conference on Mining Software Repositories, MSR 2023 - Melbourne, Australia
Duration: May 15 2023May 16 2023

Publication series

NameProceedings - 2023 IEEE/ACM 20th International Conference on Mining Software Repositories, MSR 2023

Conference

Conference20th IEEE/ACM International Conference on Mining Software Repositories, MSR 2023
Country/TerritoryAustralia
CityMelbourne
Period5/15/235/16/23

Keywords

  • Issue Tracker
  • Label
  • Machine Learning
  • Open Source Software
  • Tag
  • Task

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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