Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hanyoung Yoo
dblp:311/8810
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › fuzzing
continuous fuzzing |
0.5 | 1 | 2021 | Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANA · ASE 2021 |
Software testing
fuzzing |
0.5 | 1 | 2021 | Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANA · ASE 2021 |
Software testing › fuzzing › mutation-based fuzzing
mutation scheduling |
0.5 | 1 | 2021 | Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANA · ASE 2021 |
Methods — techniques the papers use, named apart from their topics
mutation scheduling strategies · 0.5grammar-aware mutation · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Intelligent resource prediction for SAP HANA continuous integration build workloads
Torsten Mandel, Jonathan Bader, Hanyoung Yoo, Stephan Kraft |
ICST | 3 |
| 2021 | Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANAabstractThis paper presents industrial experiences on enhancing the configurability of a fuzzing framework for effective continuous fuzzing of the SAP HANA components. We propose five new mutation scheduling strategies for effective uses of grammar-aware mutators in the unit-level fuzzing framework, and three new seed corpus selection strategies to configure a fuzzing campaign to check on changed code in priority. The empirical results show that the proposed extension gives users chances to improve fuzzing effectiveness and efficiency by configuring the framework specifically for each target component. Hanyoung Yoo, Jingun Hong, Lucas Bader, Dongwon Hwang, Shin Hong |
ASE | 1 |