A Systematic Review of Unsupervised Defect Prediction

This project is about a systematic review of unsupervised learning techniques for software defect prediction. We conducted this systematic literature review that identified 49 studies which satisfied our inclusion criteria containing 2456 individual experimental results. In order to compare prediction performance across these studies in a consistent way, we recomputed the confusion matrices and employed MCC as our main performance measure. Note that the latest version is on Mendeley: http://dx.doi.org/10.17632/h24ctmyx73.1.