Demo Data from EDBT 2026 / reviewers in the wild / expert
Gishor Sivanrupan
dblp:306/1609
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 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 |
Program analysis · 70% Software testing · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › symbolic execution
path exploration |
0.5 | 1 | 2021 | Learning to Explore Paths for Symbolic Execution · CCS 2021 |
Program analysis
symbolic execution |
0.5 | 1 | 2021 | Learning to Explore Paths for Symbolic Execution · CCS 2021 |
Software testing
test generation |
0.5 | 1 | 2021 | Learning to Explore Paths for Symbolic Execution · CCS 2021 |
Program analysis › symbolic execution
path explosion |
0.1 | 1 | 2021 | Learning to Explore Paths for Symbolic Execution · CCS 2021 |
Methods — techniques the papers use, named apart from their topics
learning-based state selection · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Learning to Explore Paths for Symbolic ExecutionabstractSymbolic execution is a powerful technique that can generate tests steering program execution into desired paths. However, the scalability of symbolic execution is often limited by path explosion, i.e., the number of symbolic states representing the paths under exploration quickly explodes as execution goes on. Therefore, the effectiveness of symbolic execution engines hinges on the ability to select and explore the right symbolic states. Gishor Sivanrupan, Petar Tsankov, Martin T. Vechev |
CCS | 2 |