Naryeong Kim

dblp:336/3943 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved

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
Empirical software engineering · 46% Debugging and program repair · 23% Software maintenance and evolution · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering › mining software repositories › commit analysis
bug-inducing commit identification
0.712023
Fonte: Finding Bug Inducing Commits from Failures · ICSE 2023
Empirical software engineering › mining software repositories › version history analysis
commit history analysis
0.712023
Fonte: Finding Bug Inducing Commits from Failures · ICSE 2023
Debugging and program repair
fault localization
0.712023
Fonte: Finding Bug Inducing Commits from Failures · ICSE 2023
Software maintenance and evolution
software evolution
0.712023
Fonte: Finding Bug Inducing Commits from Failures · ICSE 2023
Software testing
test coverage
0.212023
Fonte: Finding Bug Inducing Commits from Failures · ICSE 2023

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

neural code embedding · 0.7information retrieval · 0.7fault localization · 0.7
YearPublicationVenuePosition
2023 Fonte: Finding Bug Inducing Commits from Failures
abstract
A Bug Inducing Commit (BIC) is a commit that introduces a software bug into the codebase. Knowing the relevant BIC for a given bug can provide valuable information for debugging as well as bug triaging. However, existing BIC identification techniques are either too expensive (because they require the failing tests to be executed against previous versions for bisection) or inapplicable at the debugging time (because they require post hoc artefacts such as bug reports or bug fixes). We propose Fonte, an efficient and accurate BIC identification technique that only requires test coverage. Fonte combines Fault Localisation (FL) with BIC identification and ranks commits based on the suspiciousness of the code elements that they modified. Fonte reduces the search space of BICs using failure coverage as well as a filter that detects commits that are merely style changes. Our empirical evaluation using 130 real-world BICs shows that Fonte significantly outperforms state-of-the-art BIC identification techniques based on Information Retrieval as well as neural code embedding models, achieving at least 39% higher MRR. We also report that the ranking scores produced by Fonte can be used to perform weighted bisection, further reducing the cost of BIC identification. Finally, we apply Fonte to a large-scale industry project with over 10M lines of code, and show that it can rank the actual BIC within the top five commits for 87% of the studied real batch-testing failures, and save the BIC inspection cost by 32% on average.
Gabin An, Jingun Hong, Naryeong Kim, Shin Yoo
ICSE3