Chanseok Oh

dblp:156/6324 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorSoftware engineering, systems software and programming languages · 2 · 1 first-authorTheory of computation · 2 · 1 first-author

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
Program analysis · 67% Debugging and program repair · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Debugging and program repair
fault localization
0.212015
VERMEER: A Tool for Tracing and Explaining Faulty C Programs · ICSE (2) 2015
Program analysis › dynamic analysis
program tracing
0.212015
VERMEER: A Tool for Tracing and Explaining Faulty C Programs · ICSE (2) 2015
Program analysis
static analysis
0.212015
VERMEER: A Tool for Tracing and Explaining Faulty C Programs · ICSE (2) 2015

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

automated theorem proving · 0.2
YearPublicationVenuePosition
2018 Machine Learning-Based Restart Policy for CDCL SAT Solvers
Jia Hui (Jimmy) Liang, Chanseok Oh, Minu Mathew, Ciza Thomas, Chunxiao (Ian) Li, Vijay Ganesh 0001
SAT2
2015 VERMEER: A Tool for Tracing and Explaining Faulty C Programs
abstract
We present VERMEER, a new automated debugging tool for C. VERMEER combines two functionalities: (1) a dynamic tracer that produces a linearized trace from a faulty C program and a given test input; and (2) a static analyzer that explains why the trace fails. The tool works in phases that simplify the input program to a linear trace, which is then analyzed using an automated theorem prover to produce the explanation. The output of each phase is a valid C program. VERMEER is able to produce useful explanations of non trivial traces for real C programs within a few seconds. The tool demo can be found at http://youtu.be/E5lKHNJVerU.
Daniel Schwartz-Narbonne, Chanseok Oh, Martin Schäf, Thomas Wies
ICSE (2)2
2015 Between SAT and UNSAT: The Fundamental Difference in CDCL SAT
Chanseok Oh
SAT1
2014 Concolic Fault Localization
abstract
An integral part of all debugging activities is the task of diagnosing the cause of an error. Most existing fault diagnosis techniques rely on the availability of high quality test suites because they work by comparing failing and passing runs to identify the error cause. This limits their applicability. One alternative are techniques that statically analyze an error trace of the program without relying on additional passing runs to compare against. Particularly promising are novel proof-based approaches that leverage the advances in automated theorem proving to obtain an abstraction of the program that aids fault diagnostics. However, existing proof-based approaches still have practical limitations such as reduced scalability and dependence on complex mathematical models of programs. Such models are notoriously difficult to develop for real-world programs. Inspired by concolic testing, we propose a novel algorithm that integrates concrete execution and symbolic reasoning about the error trace to address these challenges. Specifically, we execute the error trace to obtain intermediate program states that allow us to split the trace into smaller fragments, each of which can be analyzed in isolation using an automated theorem prover. Moreover, we show how this approach can avoid complex logical encodings when reasoning about traces in low-level C programs. We have conducted an experiment where we applied our new algorithm to error traces generated from faulty versions of UNIX utils such as gzip and sed. Our experiment indicates that our concolic fault abstraction scales to real-world error traces and generates useful error diagnoses.
Chanseok Oh, Martin Schäf, Daniel Schwartz-Narbonne, Thomas Wies
SCAM1