Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mauro Bringolf
dblp:337/0851
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · unresolved
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.
| Theoretical computer science
1 paper |
Automated reasoning and model checking · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Software testing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Automated reasoning and model checking
satisfiability modulo theories |
0.6 | 1 | 2022 | Finding and Understanding Incompleteness Bugs in SMT Solvers · ASE 2022 |
Automated reasoning and model checking › satisfiability modulo theories › SMT solvers
SMT solver testing |
0.6 | 1 | 2022 | Finding and Understanding Incompleteness Bugs in SMT Solvers · ASE 2022 |
Software testing
fuzzing |
0.2 | 1 | 2022 | Finding and Understanding Incompleteness Bugs in SMT Solvers · ASE 2022 |
Software testing › fuzzing
SMT solver fuzzing |
0.2 | 1 | 2022 | Finding and Understanding Incompleteness Bugs in SMT Solvers · ASE 2022 |
Methods — techniques the papers use, named apart from their topics
local weakening · 1.1local strengthening · 1.1formula mutation · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Finding and Understanding Incompleteness Bugs in SMT SolversabstractWe propose Janus, an approach for finding incompleteness bugs in SMT solvers. The key insight is to mutate SMT formulas with local weakening and strengthening rules that preserve the satisfiability of the seed formula. The generated mutants are used to test SMT solvers for incompleteness bugs, i.e., inputs on which SMT solvers unexpectedly return unknown. We realized Janus on top of the SMT solver fuzzing framework YinYang. From June to August 2021, we stress-tested the two state-of-the-art SMT solvers Z3 and CVC5 with Janus and totally reported 31 incompleteness bugs. Out of these, 26 have been confirmed as unique bugs and 19 are already fixed by the developers. Our diverse bug findings uncovered functional, regression, and performance bugs—several triggered discussions among the developers sharing their in-depth analysis. Mauro Bringolf, Dominik Winterer, Zhendong Su 0001 |
ASE | 1 |