WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning

Tianwei Lei*, Jingfeng Xue, Yong Wang*, Zequn Niu, Zhiwei Shi, Yu Zhang

*此作品的通讯作者

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10 引用 (Scopus)

摘要

Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23% compared with TCA+ algorithm on AEEEM datasets, and an average improvement of 5% on ReLink datasets.

源语言英语
页(从-至)354-366
页数13
期刊Chinese Journal of Electronics
31
2
DOI
出版状态已出版 - 3月 2022

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