TY - JOUR
T1 - A Dynamic Model Selection Approach to Mitigate the Change of Balance Problem in Cross-Version Bug Prediction
AU - Demanou, Hiroshi
AU - Monden, Akito
AU - Tsunoda, Masateru
N1 - Funding Information:
This work was supported in part by JSPS KAKENHI Grant number JP20K11749 and JP20H05706.
Publisher Copyright:
© 2022 Copyright for this paper by its authors.
PY - 2022
Y1 - 2022
N2 - This paper focuses on the “change of balance” problem in cross-version bug prediction where the percentage of buggy modules changes between different versions. Such difference badly affects the prediction performance. To mitigate this problem, this paper employs a dynamic model selection approach equipped with two prediction models (always-buggy model and always-non-buggy model) and Bandit algorithm to select better models in each one-module-by-one prediction. An experiment with data sets of 20 releases of 10 open source software showed that the proposed approach can improve F1-measure compared with the conventional cross-version prediction.
AB - This paper focuses on the “change of balance” problem in cross-version bug prediction where the percentage of buggy modules changes between different versions. Such difference badly affects the prediction performance. To mitigate this problem, this paper employs a dynamic model selection approach equipped with two prediction models (always-buggy model and always-non-buggy model) and Bandit algorithm to select better models in each one-module-by-one prediction. An experiment with data sets of 20 releases of 10 open source software showed that the proposed approach can improve F1-measure compared with the conventional cross-version prediction.
KW - Bandit algorithm
KW - defect-prone module prediction
KW - software quality assurance
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M3 - Conference article
AN - SCOPUS:85147004462
SN - 1613-0073
VL - 3330
SP - 4
EP - 9
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - Joint of the 10th International Workshop on Quantitative Approaches to Software Quality and the 6th Software Engineering Education Workshop, QuASoQ-SEED 2022
Y2 - 6 December 2022
ER -