Soonhwang Choi

dblp:58/821 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 2015
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 first-authorSoftware engineering, systems software and programming languages · 2Databases, data management, data science and information retrieval · 1 · 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
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 FSE4
2012 A rule-based approach for estimating software development cost using function point and goal and scenario based requirements
Soonhwang Choi, Sooyong Park, Vijayan Sugumaran
Expert Syst. Appl.1
2007 Using classification techniques for informal requirements in the requirements analysis-supporting system
Youngjoong Ko, Sooyong Park, Jungyun Seo, Soonhwang Choi
Inf. Softw. Technol.4
2006 Function Point Extraction Method from Goal and Scenario Based Requirements Text
Soonhwang Choi, Sooyong Park, Vijayan Sugumaran
NLDB1