CITATION.cff

@inproceedings{10.1145/3551349.3559517,
author = {Synovic, Nicholas M. and Hyatt, Matt and Sethi, Rohan and Thota, Sohini and Shilpika and Miller, Allan J. and Jiang, Wenxin and Amobi, Emmanuel S. and Pinderski, Austin and L\"{a}ufer, Konstantin and Hayward, Nicholas J. and Klingensmith, Neil and Davis, James C. and Thiruvathukal, George K.},
title = {Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics},
year = {2023},
isbn = {9781450394758},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3551349.3559517},
doi = {10.1145/3551349.3559517},
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 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.},
booktitle = {Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering},
articleno = {167},
numpages = {4},
keywords = {Empirical software engineering, Software metrics},
location = {Rochester, MI, USA},
series = {ASE '22}
}