In the field of software engineering, software defect prediction (SDP) in early stages is vital for software reliability and quality. As such, identifying defective modules in early stages is necessary to aid software testers in detecting modules that required intensive testing. Software development teams can detect bugs by analyzing software testing results, but it is costly and time-consuming by testing entire software modules. Hence, they increase maintenance costs and efforts to resolve them. Defects negatively affect software quality and software reliability. Software defects are programming errors that may occur because of errors in the source code, requirements, or design. Machine Learning, Ensembles, Prediction, Software Metrics, Software DefectĪ software defect is a bug, fault, or error in a program that causes improper outcomes. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Received: ApAccepted: Published: May 21, 2019Īn essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, KSAĬopyright © 2019 by author(s) and Scientific Research Publishing Inc.
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