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A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction
[article]
2020
arXiv
pre-print
Unsupervised machine learners have been increasingly applied to software defect prediction. It is an approach that may be valuable for software practitioners because it reduces the need for labeled training data. Objective: Investigate the use and performance of unsupervised learning techniques in software defect prediction. Method: We conducted a systematic literature review that identified 49 studies containing 2456 individual experimental results, which satisfied our inclusion criteria
arXiv:1907.12027v4
fatcat:q2o5ew5zhra5lauyebd3hl65uy