Demo Data from EDBT 2026 / reviewers in the wild / expert
Bongjun Jang
dblp:193/4407
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0004-5099-9526ORCID · 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 |
Software testing · 93% Compilers and program optimization · 7% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
compiler testing |
0.9 | 1 | 2025 | Optimization-Directed Compiler Fuzzing for Continuous Translation Validation · Proc. ACM Program. Lang. 2025 |
Software testing › fuzzing › greybox fuzzing
directed greybox fuzzing |
0.9 | 1 | 2025 | Optimization-Directed Compiler Fuzzing for Continuous Translation Validation · Proc. ACM Program. Lang. 2025 |
Software testing
fuzzing |
0.9 | 1 | 2025 | Optimization-Directed Compiler Fuzzing for Continuous Translation Validation · Proc. ACM Program. Lang. 2025 |
Software testing › compiler testing
miscompilation detection |
0.9 | 1 | 2025 | Optimization-Directed Compiler Fuzzing for Continuous Translation Validation · Proc. ACM Program. Lang. 2025 |
Compilers and program optimization › verified compilation
translation validation |
0.3 | 1 | 2025 | Optimization-Directed Compiler Fuzzing for Continuous Translation Validation · Proc. ACM Program. Lang. 2025 |
Methods — techniques the papers use, named apart from their topics
translation validation · 0.9fuzzing · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Optimization-Directed Compiler Fuzzing for Continuous Translation ValidationabstractIncorrect compiler optimizations can lead to unintended program behavior and security vulnerabilities. However, the enormous size and complexity of modern compilers make it challenging to ensure the correctness of optimizations. The problem becomes more severe as compiler engineers continuously add new optimizations to improve performance and support new language features. In this paper, we propose Optimuzz , a framework to effectively detect incorrect optimization bugs in such continuously changing compilers. The key idea is to combine two complementary techniques: directed grey-box fuzzing and translation validation. We design a novel optimization-directed fuzzing framework that efficiently generates input programs to trigger specific compiler optimizations. Optimuzz then uses existing translation validation tools to verify the correctness of the optimizations on the input programs. We instantiate our approach for two major compilers, LLVM and TurboFan . The results show that Optimuzz can effectively detect miscompilation bugs in these compilers, outperforming state-of-the-art tools. We also applied Optimuzz to the latest version of LLVM and discovered 55 new miscompilation bugs. Jaeseong Kwon, Bongjun Jang, Juneyoung Lee, Kihong Heo |
Proc. ACM Program. Lang. | 2 |