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Naoyasu Ubayashi
Researcher at Kyushu University
Publications - 122
Citations - 2125
Naoyasu Ubayashi is an academic researcher from Kyushu University. The author has contributed to research in topics: Software development & Context (language use). The author has an hindex of 17, co-authored 113 publications receiving 1660 citations. Previous affiliations of Naoyasu Ubayashi include Toshiba & University of Tokyo.
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Journal ArticleDOI
A large-scale empirical study of just-in-time quality assurance
Yasutaka Kamei,Emad Shihab,Bram Adams,Ahmed E. Hassan,Audris Mockus,Anand Sinha,Naoyasu Ubayashi +6 more
TL;DR: The findings indicate that “Just-In-Time Quality Assurance” may provide an effort-reducing way to focus on the most risky changes and thus reduce the costs of developing high-quality software.
Journal ArticleDOI
Studying just-in-time defect prediction using cross-project models
Yasutaka Kamei,Takafumi Fukushima,Shane McIntosh,Kazuhiro Yamashita,Naoyasu Ubayashi,Ahmed E. Hassan +5 more
TL;DR: An empirical study on 11 open source projects finds that while JIT models rarely perform well in a cross-project context, their performance tends to improve when using approaches that select models trained using other projects that are similar to the testing project, and combine the models of several other projects to produce an ensemble model.
Proceedings ArticleDOI
An empirical study of just-in-time defect prediction using cross-project models
TL;DR: JIT cross-project models learned using other projects are a viable solution for projects with little historical data, but perform best when the data used to learn them is carefully selected.
Proceedings ArticleDOI
DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction
TL;DR: This paper proposes an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects.
Proceedings ArticleDOI
Association aspects
TL;DR: A linguistic mechanism for AspectJ-like languages that concisely associates aspect instances to object groups is proposed, which supports association aspects and provides a new pointcut primitive to specify aspect instances as execution contexts of advice.