Prediction of Residual Defects after Code Review Based on Reviewer Confidence
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E107-D No.3 pp.273-276
- Publication Date
- 2024/03/01
- Publicized
- 2023/12/08
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023MPL0002
- Type of Manuscript
- Special Section LETTER (Special Section on Empirical Software Engineering)
- Category
Authors
Shin KOMEDA
Kindai University
Masateru TSUNODA
Kindai University
Keitaro NAKASAI
Osaka Metropolitan University College of Technology
Hidetake UWANO
Nara College
Keyword
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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
Copy
@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},}
Copy
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 -
English
