An essential objective of software development is to locate and fix defects\nahead of schedule that could be expected under diverse circumstances. Many\nsoftware development activities are performed by individuals, which may lead\nto different software bugs over the development to occur, causing disappointments\nin the not-so-distant future. Thus, the prediction of software defects\nin the first stages has become a primary interest in the field of software\nengineering. Various software defect prediction (SDP) approaches that rely\non software metrics have been proposed in the last two decades. Bagging,\nsupport vector machines (SVM), decision tree (DS), and random forest (RF)\nclassifiers are known to perform well to predict defects. This paper studies\nand compares these supervised machine learning and ensemble classifiers on\n10 NASA datasets. The experimental results showed that, in the majority of\ncases, RF was the best performing classifier compared to the others.
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