Demo Data from EDBT 2026 / reviewers in the wild / expert
Sohini Thota
dblp:325/4387
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-3496-8867ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
mining software repositories |
0.6 | 1 | 2022 | Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics · ASE 2022 |
Empirical software engineering
software metrics |
0.6 | 1 | 2022 | Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics · ASE 2022 |
Methods — techniques the papers use, named apart from their topics
metric visualization · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal MetricsabstractSoftware 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. Nicholas Synovic, Matt Hyatt, Rohan Sethi, Sohini Thota, Shilpika, Allan J. Miller, Wenxin Jiang 0001, Emmanuel S. Amobi, Austin Pinderski, Konstantin Läufer, Nicholas J. Hayward, Neil Klingensmith, James C. Davis 0001, George K. Thiruvathukal |
ASE | 4 |