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Jaehyuk Yeon

dblp:167/7863 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Software engineering, systems software and programming languages · 1

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 · 56% Empirical software engineering · 44%

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

TopicWeightPapersLastEvidence papers
Software testing
API testing
0.212015
REMI: defect prediction for efficient API testing · ESEC/SIGSOFT FSE 2015
Empirical software engineering › mining software repositories
defect prediction
0.212015
REMI: defect prediction for efficient API testing · ESEC/SIGSOFT FSE 2015
Software testing › regression testing
test case prioritization
0.112015
REMI: defect prediction for efficient API testing · ESEC/SIGSOFT FSE 2015

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

defect prediction · 0.2
YearPublicationVenuePosition
2015 REMI: defect prediction for efficient API testing
abstract
Quality assurance for common APIs is important since the the reliability of APIs affects the quality of other systems using the APIs. Testing is a common practice to ensure the quality of APIs, but it is a challenging and laborious task especially for industrial projects. Due to a large number of APIs with tight time constraints and limited resources, it is hard to write enough test cases for all APIs. To address these challenges, we present a novel technique, REMI that predicts high risk APIs in terms of producing potential bugs. REMI allows developers to write more test cases for the high risk APIs. We evaluate REMI on a real-world industrial project, Tizen-wearable, and apply REMI to the API development process at Samsung Electronics. Our evaluation results show that REMI predicts the bug-prone APIs with reasonable accuracy (0.681 f-measure on average). The results also show that applying REMI to the Tizen-wearable development process increases the number of bugs detected, and reduces the resources required for executing test cases.
Mijung Kim, Jaechang Nam, Jaehyuk Yeon, Soonhwang Choi, Sunghun Kim 0001
ESEC/SIGSOFT FSE3