VLDB 2026 Research / reviewers in the wild / expert
Shubham Vasudeo Desai
dblp:426/3123
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 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 |
Software testing · 62% Software maintenance and evolution · 38% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › release engineering
continuous integration |
0.9 | 1 | 2025 | PrioTestCI: Efficient Test Case Prioritization in GitHub Workflows for CI Optimization · ASE 2025 |
Software testing › regression testing
test case prioritization |
0.9 | 1 | 2025 | PrioTestCI: Efficient Test Case Prioritization in GitHub Workflows for CI Optimization · ASE 2025 |
Software testing
regression testing |
0.3 | 1 | 2025 | PrioTestCI: Efficient Test Case Prioritization in GitHub Workflows for CI Optimization · ASE 2025 |
Software testing › regression testing
test selection |
0.3 | 1 | 2025 | PrioTestCI: Efficient Test Case Prioritization in GitHub Workflows for CI Optimization · ASE 2025 |
Methods — techniques the papers use, named apart from their topics
commit-to-commit test result tracking · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | PrioTestCI: Efficient Test Case Prioritization in GitHub Workflows for CI OptimizationabstractContinuous Integration (CI) is a widely adopted practice in software development to automatically verify code changes across diverse environments. However, executing the full test suite on every pull request update can lead to redundant runs, slower feedback loops, and inefficient utilization of CI resources. To address this issue, we introduce PrioTestCI, a prioritization technique within GitHub Actions that focuses on re-executing test cases that have previously failed. If these prioritized tests succeed, the remaining tests proceed; otherwise, the workflow terminates early, saving computation resources and providing early feedback to developers. PrioTestCI utilizes commit-to-commit test result tracking to inform future test runs, thereby reducing unnecessary repetition and accelerating validation cycles. We evaluated our technique on the Pytest project, a real-world open-source project with an extensive test matrix. PrioTestCI resulted in a CI runtime reduction of 1h57m39s compared to the normal workflow, with individual configuration improvements ranging from 63.75% to 91.94% (81.55% on average). Demo video: https://youtu.be/_3CF9LJdv0I?si=XyE_8mBnDxk1lMnD Repository: https://github.com/ShubhamDesai/CI-Optimization Shubham Vasudeo Desai, Shonil Bhide, Souhaila Serbout, Luciano Marchezan, Wesley K. G. Assunção |
ASE | 1 |