EDBT 2026 Demo / reviewers in the wild / expert
Soonhwang Choi
dblp:58/821
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
API testing |
0.2 | 1 | 2015 | REMI: defect prediction for efficient API testing · ESEC/SIGSOFT FSE 2015 |
Empirical software engineering › mining software repositories
defect prediction |
0.2 | 1 | 2015 | REMI: defect prediction for efficient API testing · ESEC/SIGSOFT FSE 2015 |
Software testing › regression testing
test case prioritization |
0.1 | 1 | 2015 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | REMI: defect prediction for efficient API testingabstractQuality 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 FSE | 4 |
| 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 |
NLDB | 1 |