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

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

Conference Paper ASE 2022 Process Metrics

Authors#

Nicholas M. Synovic
Matt Hyatt
Rohan Sethi
Sohini Thota
Shilpika
Allan J. Miller
Emmanuel S. Amobi
Austin Pinderski
Konstantin Läufer
Nicholas J. Hayward
Neil Klingensmith
James C. Davis
George K. Thiruvathukal

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 SoftwareSystemsLaboratory/prime.

Artifacts#

Todo

  • Add the paper preprint

  • Add the poster

  • Add link to the source code

  • Update the bibtex

Paper Preprint

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Published Paper

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Poster

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Source Code

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BibTex
@inproceedings{synovic_snapshot_2023,
   address = {New York, NY, USA},
   series = {{ASE} '22},
   title = {Snapshot {Metrics} {Are} {Not} {Enough}: {Analyzing} {Software} {Repositories} with {Longitudinal} {Metrics}},
   isbn = {978-1-4503-9475-8},
   shorttitle = {Snapshot {Metrics} {Are} {Not} {Enough}},
   url = {https://dl.acm.org/doi/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.},
   urldate = {2023-09-06},
   booktitle = {Proceedings of the 37th {IEEE}/{ACM} {International} {Conference} on {Automated} {Software} {Engineering}},
   publisher = {Association for Computing Machinery},
   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 Läufer, Konstantin and Hayward, Nicholas J. and Klingensmith, Neil and Davis, James C. and Thiruvathukal, George K.},
   month = jan,
   year = {2023},
   keywords = {Empirical software engineering, Software metrics},
   pages = {1--4},
}

Note

I have continued to iterate upon the PRIME tool in a new repository. If want to learn more, visit NicholasSynovic/prime.

Video#

Contents