H
Hakjoo Oh
Researcher at Korea University
Publications - 67
Citations - 1333
Hakjoo Oh is an academic researcher from Korea University. The author has contributed to research in topics: Static analysis & Program analysis. The author has an hindex of 16, co-authored 59 publications receiving 877 citations. Previous affiliations of Hakjoo Oh include Seoul National University.
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Proceedings ArticleDOI
VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery
TL;DR: VUDDY outperformed four state-of-the-art code clone detection techniques in terms of both scalability and accuracy, and proved its effectiveness by detecting zero-day vulnerabilities in widely used software systems, such as Apache HTTPD and Ubuntu OS Distribution.
Proceedings ArticleDOI
Design and implementation of sparse global analyses for C-like languages
TL;DR: A general method for achieving global static analyzers that are precise, sound, yet also scalable, and to support relational as well as non-relational semantics properties for C-like languages is presented.
Proceedings ArticleDOI
Selective context-sensitivity guided by impact pre-analysis
TL;DR: This method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries, and demonstrates generality by following the same principle and developing a selective relational analysis.
Proceedings ArticleDOI
VERISMART: A Highly Precise Safety Verifier for Ethereum Smart Contracts
TL;DR: VerISMART as discussed by the authors is a highly precise verifier for ensuring arithmetic safety of Ethereum smart contracts, which is able to automatically discover and leverage transaction invariants that are essential for precisely analyzing smart contracts.
Proceedings ArticleDOI
Learning a strategy for adapting a program analysis via bayesian optimisation
TL;DR: This paper presents a new approach for building an adaptive static analyser that includes a sophisticated parameterised strategy that decides, for each part of a given program, whether to apply a precision-improving technique to that part or not, and presents a method for learning a good parameter for such a strategy from an existing codebase via Bayesian optimisation.