VLDB 2026 Research / reviewers in the wild / expert
Md Nakhla Rafi
dblp:359/6012
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0005-4707-8985ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 2 first-author · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | SBEST: Spectrum-based fault localization without fault-triggering tests
Md Nakhla Rafi, Lorena Barreto Simedo Pacheco, An Ran Chen, Jinqiu Yang 0001, Tse-Hsun (Peter) Chen |
Empir. Softw. Eng. | 1 |
| 2025 | Revisiting Defects4J for Fault Localization in Diverse Development ScenariosabstractDefects4J stands out as a leading benchmark dataset for software testing research, providing a controlled environment to study real bugs from prominent open-source systems. While Defects4J provides a clean and valuable dataset, we aim to explore how fault localization techniques perform under less-controlled development scenarios. In this paper, we revisited Defects4J to study developers’ changes to fault-triggering tests after the bugs were reported/fixed. We aim to introduce a new evaluation scenario within Defects4J, focusing on the implications of regression tests and test changes added after the bug was fixed. We analyze when these tests were modified relative to bug report creation and examine spectrum-based fault localization (SBFL) performance in less-controlled settings. Our findings show that 1) 55% of the fault-triggering tests were added to replicate the bug or test for regression; 2) 22% of the tests were changed after the bug reports, incorporating information related to the bug; 3) developers often update tests with new assertions or changes to match source code updates; and 4) SBFL performance differs significantly in less-controlled settings (down by at most 90% for Mean First Rank). Our study points out the diverse development scenarios in the studied bugs, highlighting new settings for future SBFL evaluations and bug benchmarks. Md Nakhla Rafi, An Ran Chen, Tse-Hsun (Peter) Chen, Shaohua Wang 0002 |
MSR | 1 |