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- KOMEDA Shin
- Kindai University
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- TSUNODA Masateru
- Kindai University
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- NAKASAI Keitaro
- College of Technology, Osaka Metropolitan University College of Technology
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- UWANO Hidetake
- National Institute of Technology, Nara College
Abstract
<p>A major approach to enhancing software quality is reviewing the source code to identify defects. To aid in identifying flaws, an approach in which a machine learning model predicts residual defects after implementing a code review is adopted. After the model has predicted the existence of residual defects, a second-round review is performed to identify such residual flaws. To enhance the prediction accuracy of the model, information known to developers but not recorded as data is utilized. Confidence in the review is evaluated by reviewers using a 10-point scale. The assessment result is used as an independent variable of the prediction model of residual defects. Experimental results indicate that confidence improves the prediction accuracy.</p>
Journal
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E107.D (3), 273-276, 2024-03-01
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390862268821749888
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- ISSN
- 17451361
- 09168532
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed