VLDB 2026 Research / reviewers in the wild / expert
Venkatesh Vobbilisetti
dblp:360/7271
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
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 · 46% Debugging and program repair · 46% Software maintenance and evolution · 7% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Debugging and program repair
automated program repair |
0.7 | 1 | 2023 | Minecraft: Automated Mining of Software Bug Fixes with Precise Code Context · ASE 2023 |
Debugging and program repair
bug fix datasets |
0.7 | 1 | 2023 | Minecraft: Automated Mining of Software Bug Fixes with Precise Code Context · ASE 2023 |
Empirical software engineering › mining software repositories › commit analysis
bug-fix mining |
0.7 | 1 | 2023 | Minecraft: Automated Mining of Software Bug Fixes with Precise Code Context · ASE 2023 |
Empirical software engineering
mining software repositories |
0.7 | 1 | 2023 | Minecraft: Automated Mining of Software Bug Fixes with Precise Code Context · ASE 2023 |
Methods — techniques the papers use, named apart from their topics
repository mining · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Minecraft: Automated Mining of Software Bug Fixes with Precise Code ContextabstractRepository mining of bug fixes from version control systems like GitHub is a challenging problem as far as the precision of the bug context is concerned, i.e., source codes immediately preceding and succeeding the fix location. Coupled with this, identification of the type of the bug fix goes a long way towards creating high quality datasets that can be used for several downstream tasks. However, existing bug fix datasets suffer from the following limitations that dilute the data quality. Firstly, they do not focus on multilingual projects in their entirety given that most open-source projects are now multilingual. Secondly, the granularity of the bug fixes are considered only at the function/method level without specifying line/statement level information. Thirdly, bug fixes lying within the scope of a source file but outside any of its constituent functions have not been examined. In this paper, we propose a solution to overcome the aforementioned limitations by introducing a novel and extensive dataset named Minecraft. With a size of 28.8GB (considering 416 GitHub projects encompassing programming languages such as C, C++, Java, and Python, 2.2M commits, 3.29M bug-fix pairs), Minecraft surpasses the existing datasets by 4-fold enlargement in terms of data availability. We believe Minecraft would serve as a valuable resource for various stakeholders in the software development and research communities, empowering them to improve software quality, develop innovative bug detection and auto-fix techniques, and advance the field of software engineering. Sai Krishna Avula, Venkatesh Vobbilisetti, Shouvick Mondal |
ASE | 2 |