Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics

July 24, 2022 Β· Entered Twilight Β· πŸ› International Conference on Automated Software Engineering

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Repo contents: .github, .gitignore, .pylintrc, .zenodo.json, CITATION.cff, LICENSE, README.md, clime_metrics, setup.py

Authors Nicholas Synovic, Matt Hyatt, Rohan Sethi, Sohini Thota, Shilpika, Allan J. Miller, Wenxin Jiang, Emmanuel S. Amobi, Austin Pinderski, Konstantin LΓ€ufer, Nicholas J. Hayward, Neil Klingensmith, James C. Davis, George K. Thiruvathukal arXiv ID 2207.11767 Category cs.SE: Software Engineering Citations 6 Venue International Conference on Automated Software Engineering Repository https://github.com/SoftwareSystemsLaboratory/prime Last Checked 1 month ago
Abstract
Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. However, existing metrics tools measure only a snapshot of a software project. Little attention has been given to enabling engineers to reason about metric trends over time -- longitudinal metrics that give insight about process, not just product. In this work, we present PRiME (PRocess MEtrics), a tool for computing and visualizing process metrics. The currently-supported metrics include productivity, issue density, issue spoilage, and bus factor. We illustrate the value of longitudinal data and conclude with a research agenda. The tool's demo video can be watched at https://youtu.be/YigEHy3_JCo. The source code can be found at https://github.com/SoftwareSystemsLaboratory/prime.
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