A Dynamic Model Selection Approach to Mitigate the Change of Balance Problem in Cross-Version Bug Prediction

Hiroshi Demanou, Akito Monden, Masateru Tsunoda

研究成果査読

抄録

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.

本文言語English
ページ(範囲)4-9
ページ数6
ジャーナルCEUR Workshop Proceedings
3330
出版ステータスPublished - 2022
イベントJoint of the 10th International Workshop on Quantitative Approaches to Software Quality and the 6th Software Engineering Education Workshop, QuASoQ-SEED 2022 - Virtual, Online
継続期間: 12月 6 2022 → …

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)

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