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Kyutae Cho

dblp:405/6291 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0008-4037-1486ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
fuzzing
0.912025
ZigZagFuzz: Interleaved Fuzzing of Program Options and Files · ACM Trans. Softw. Eng. Methodol. 2025
Software testing
test coverage
0.312025
ZigZagFuzz: Interleaved Fuzzing of Program Options and Files · ACM Trans. Softw. Eng. Methodol. 2025

Methods — techniques the papers use, named apart from their topics

interleaved mutation · 0.9corpus shrinking · 0.9
YearPublicationVenuePosition
2026 Industrial Application of Deep Learning based Fault Localization with Mutation Features
Heechan Yang, Ahcheong Lee, Kyutae Cho, Yunsam Kim
ICST3
2025 ZigZagFuzz: Interleaved Fuzzing of Program Options and Files
abstract
Command-line options (e.g., -l , -F , -R for ls ) given to a command-line program can significantly alternate the behaviors of the program. Thus, fuzzing not only file input but also program options can improve test coverage and bug detection. In this article, we propose ZigZagFuzz which achieves higher test coverage and detects more bugs than the state-of-the-art fuzzers by separately mutating program options and file inputs in an iterative/interleaving manner. ZigZagFuzz applies the following three core ideas. First, to utilize different characteristics of the program option domain and the file input domain, ZigZagFuzz separates phases of mutating program options from ones of mutating file inputs and performs two distinct mutation strategies on the two different domains. Second, to reach deep segments of a target program that are accessed through an interleaving sequence of program option checks and file inputs checks, ZigZagFuzz continuously interleaves phases of mutating program options with phases of mutating file inputs. Finally, to improve fuzzing performance further, ZigZagFuzz periodically shrinks input corpus by removing similar test inputs based on their function coverage. The experiment results on the 20 real-world programs show that ZigZagFuzz improves test coverage and detects 1.9 to 10.6 times more bugs than the state-of-the-art fuzzers that mutate program options such as AFL++-argv, AFL++-all, Eclipser, CarpetFuzz, ConfigFuzz, and POWER. We have reported the new bugs detected by ZigZagFuzz, and the original developers confirmed our bug reports.
Ahcheong Lee, Youngseok Choi, Shin Hong, Kyutae Cho, Moonzoo Kim
ACM Trans. Softw. Eng. Methodol.5