Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ana Cavalcante-Studart
dblp:375/9734
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0007-3876-0629ORCID · reported
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 |
Program analysis · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › symbolic execution
path explosion |
0.8 | 1 | 2024 | Interprocedural Path Complexity Analysis · ISSTA 2024 |
Program analysis
symbolic execution |
0.8 | 1 | 2024 | Interprocedural Path Complexity Analysis · ISSTA 2024 |
Methods — techniques the papers use, named apart from their topics
interprocedural analysis · 0.8asymptotic path complexity · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Interprocedural Path Complexity AnalysisabstractSoftware testing techniques like symbolic execution face significant challenges with path explosion. Asymptotic Path Complexity (APC) quantifies this path explosion complexity, but existing APC methods do not work for interprocedural functions in general. Our new algorithm, APC-IP, efficiently computes APC for a wider range of functions, including interprocedural ones, improving over previous methods in both speed and scope. We implement APC-IP atop the existing software Metrinome, and test it against a benchmark of C functions, comparing it to existing and baseline approaches as well as comparing it to the path explosion of the symbolic execution engine Klee. The results show that APC-IP not only aligns with previous APC values but also excels in performance, scalability, and handling complex source code. It also provides a complexity prediction of the number of paths explored by Klee, extending the APC metric's applicability and surpassing previous implementations. Mira Bhagirathi Kaniyur, Ana Cavalcante-Studart, Sangeon Park, Duy Lam, Lucas Bang |
ISSTA | 2 |