On the relative value of clustering techniques for Unsupervised Effort-Aware Defect Prediction

Peixin Yang, Lin Zhu, Yanjiao Zhang, Chuanxiang Ma, Liming Liu, Xiao Yu, Wenhua Hu

Abstract: Highlights•Examine effect of clustering for unsupervised effort-aware defect prediction.•41 datasets from the PROMISE repository are taken to conduct experiments.•K-medoids performs best on unsupervised effort-aware defect prediction.