The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Une approche majeure pour améliorer la qualité des logiciels consiste à examiner le code source pour identifier les défauts. Pour faciliter l'identification des défauts, une approche dans laquelle un modèle d'apprentissage automatique prédit les défauts résiduels après la mise en œuvre d'une révision du code est adoptée. Une fois que le modèle a prédit l’existence de défauts résiduels, un deuxième examen est effectué pour identifier ces défauts résiduels. Pour améliorer la précision des prévisions du modèle, les informations connues des développeurs mais non enregistrées sous forme de données sont utilisées. La confiance dans l'évaluation est évaluée par les évaluateurs à l'aide d'une échelle de 10 points. Le résultat de l'évaluation est utilisé comme variable indépendante du modèle de prédiction des défauts résiduels. Les résultats expérimentaux indiquent que la confiance améliore la précision des prévisions.
Shin KOMEDA
Kindai University
Masateru TSUNODA
Kindai University
Keitaro NAKASAI
Osaka Metropolitan University College of Technology
Hidetake UWANO
Nara College
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Shin KOMEDA, Masateru TSUNODA, Keitaro NAKASAI, Hidetake UWANO, "Prediction of Residual Defects after Code Review Based on Reviewer Confidence" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 3, pp. 273-276, March 2024, doi: 10.1587/transinf.2023MPL0002.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023MPL0002/_p
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@ARTICLE{e107-d_3_273,
author={Shin KOMEDA, Masateru TSUNODA, Keitaro NAKASAI, Hidetake UWANO, },
journal={IEICE TRANSACTIONS on Information},
title={Prediction of Residual Defects after Code Review Based on Reviewer Confidence},
year={2024},
volume={E107-D},
number={3},
pages={273-276},
abstract={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.},
keywords={},
doi={10.1587/transinf.2023MPL0002},
ISSN={1745-1361},
month={March},}
Copier
TY - JOUR
TI - Prediction of Residual Defects after Code Review Based on Reviewer Confidence
T2 - IEICE TRANSACTIONS on Information
SP - 273
EP - 276
AU - Shin KOMEDA
AU - Masateru TSUNODA
AU - Keitaro NAKASAI
AU - Hidetake UWANO
PY - 2024
DO - 10.1587/transinf.2023MPL0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2024
AB - 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.
ER -