Prediction of Residual Defects after Code Review Based on Reviewer Confidence
Author:
Affiliation:
1. Kindai University
2. College of Technology, Osaka Metropolitan University College of Technology
3. National Institute of Technology, Nara College
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/3/E107.D_2023MPL0002/_pdf
Reference12 articles.
1. [1] B. Floyd, T. Santander, and W. Weimer, “Decoding the representation of code in the brain: an fMRI study of code review and expertise,” Proc. International Conference on Software Engineering (ICSE), pp.175-186, 2017. 10.1109/icse.2017.24
2. [2] H. Hijazi, J. Duraes, R. Couceiro, J. Castelhano, R. Barbosa, J. Medeiros, M. Castelo-Branco, P. De Carvalho, and H. Madeira, “Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension,” IEEE Trans. Softw. Eng., vol.49, no.2, pp.626-645, 2023. 10.1109/TSE.2022.3158543
3. [3] M. Höst, B. Regnell, and C. Wohlin, “Using Students as Subjects — A Comparative Study ofStudents and Professionals in Lead-Time Impact Assessment,” Empirical Software Engineering, vol.5, no.3, pp.201-214, 2000. 10.1023/A:1026586415054
4. [4] R. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, Otexts, 2021.
5. [5] Information-technology Promotion Agency (IPA), Guidebook of devemopment methods to achieve high reliablility software, IPA, 2011 (in Japanese).
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