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Summary
This summary is machine-generated.

Researchers developed GrimoireLab, a reusable toolset for mining software repositories. This toolset automates data retrieval, curation, and visualization, enhancing data analysis reproducibility and efficiency for software development studies.

Keywords:
DatasetsEmpirical software engineeringMining software repositoriesSoftware analyticsSoftware developmentSoftware development visualizationToolset

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Area of Science:

  • Software Engineering
  • Data Mining
  • Information Retrieval

Background:

  • Extensive research exists on software repositories, with established knowledge for data retrieval, storage, and basic analytics.
  • Existing reusable tools for software repository analysis still have room for improvement in implementing this knowledge effectively.

Purpose of the Study:

  • To create a reusable toolset for common tasks in software repository data analysis.
  • To facilitate data retrieval, curation, and visualization for reproducible datasets.
  • To automate routine tasks for researchers and analysts in software engineering.

Main Methods:

  • Leveraging domain expertise to identify key scenarios and components for a reusable toolset.
  • Developing toolset components based on identified needs.
  • Iteratively refining components through feedback from commercial, community, and academic use.

Main Results:

  • Developed GrimoireLab, an efficient toolset with five core components.
  • Supports approximately 30 diverse software development data sources.
  • Features a common API, data source integration, semi-structured storage, and basic visualization/analysis tools.

Conclusions:

  • GrimoireLab is a mature, field-tested toolset for mining software repositories.
  • It enhances the reproducibility and comparability of software engineering studies.
  • The toolset is expected to significantly reduce the effort required for data analysis and service provision in this domain.