Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics
Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics
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快照指标还不够：使用纵向指标分析软件存储库
DOI：
10.1145/3551349.3559517
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Läufer, Konstantin
中科院分区：
文献类型：
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作者：
Synovic, Nicholas M.;Hyatt, Matt;Sethi, Rohan;Thota, Sohini;Shilpika;Miller, Allan J.;Jiang, Wenxin;Amobi, Emmanuel S.;Pinderski, Austin;Läufer, Konstantin
软件度量捕获有关软件开发过程和产品的信息。这些度量支持例如在团队管理或依赖关系选择中的决策。然而，现有的度量工具仅衡量软件项目的快照。很少有人注意到让工程师能够推理随着时间推移的指标趋势--提供对过程的洞察的纵向指标，而不仅仅是产品。在这项工作中，我们介绍了Prime(过程度量)，这是一个计算和可视化过程度量的工具。目前支持的指标包括工作效率、问题密度、问题损坏和总线系数。我们说明了纵向数据的价值，并以研究议程结束。该工具的演示视频可在https://bit.ly/ase2022-prime.上观看源代码可在https://github.com/SoftwareSystemsLaboratory/prime.上找到
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 to compute and visualize 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://bit.ly/ase2022-prime. Source code can be found at https://github.com/SoftwareSystemsLaboratory/prime.
研究软件开发中软件度量使用的调查
DOI：
10.1109/escience.2018.00036
发表时间：
2018
期刊：
2018 IEEE 14th International Conference on e-Science (e-Science)
影响因子：
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作者：
Nasir U. Eisty;G. Thiruvathukal;Jeffrey C. Carver
通讯作者：
Jeffrey C. Carver