Document Type
Conference Proceeding
Publication Date
1-5-2023
Publication Title
ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Pages
1-4
Publisher Name
Association for Computing Machinery
Publisher Location
New York, NY
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 this https URL. The source code can be found at this https URL.
Identifier
ISBN: 978-1-4503-9475-8
Recommended Citation
Nicholas Synovic, Matt Hyatt, Rohan Sethi, Sohini Thota, Shilpika, Allan J. Miller, Wenxin Jiang, Emmanuel S. Amobi, Austin Pinderski, Konstantin Läufer, Nicholas J. Hayward, Neil Klingensmith, James C. Davis, George K. Thiruvathukal, "Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics", Proceedings of Automated Software Engineering 2022.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Statement
© 2022 Association for Computing Machinery.
Comments
Author Posting. © Association for Computing Machinery, 2022. This is the author's version of the work. It is posted here by permission of the Association for Computing Machinery for personal use, not for redistribution. The definitive version was published in ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Article No. 167, January 5 2023. https://doi.org/10.1145/3551349.3559517