A Method For Combining Personalized Bug Prediction Models Toward More Accurate Bug Prediction and Its Evaluation
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- MIYAMOTO Atsuya
- Graduate School of Sc. and Eng., Ehime University
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- AMAN Hirohisa
- Center for Information Technology, Ehime University
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- KAWAHARA Minoru
- Center for Information Technology, Ehime University
Bibliographic Information
- Other Title
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- バグ混入予測の精度向上に向けた個人化予測モデルの組合せ手法とその評価
Abstract
<p>Source code changes are essential for software evolution. However, a source code change also has a risk of introducing a new bug into the software. To predict such a bug by using features of the code change, researchers have studied various bug prediction models in the past. One of the most noteworthy methods is the personalized bug prediction, which builds a prediction model customized for each developer. Although the personalized bug prediction model is a promising one, it also has a challenge that we cannot make appropriate models for less experienced developers due to the lack of code change (commit) data. Toward a resolution of the challenge, this paper proposes a method for combining two or more personalized bug prediction models. The usefulness of the proposed method is proved by an empirical study with the data from five open-source software development projects.</p>
Journal
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- Computer Software
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Computer Software 37 (4), 4_38-4_49, 2020-10-23
Japan Society for Software Science and Technology
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Details 詳細情報について
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- CRID
- 1391694356245146624
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- NII Article ID
- 130007959060
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- ISSN
- 02896540
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed