Title: Classifying defective software projects based on machine learning and complexity metrics

Authors: Mustafa Hammad

Addresses: Department of Computer Science, Mutah University, Al-Karak, Mutah, 61710, Jordan

Abstract: Software defects can lead to software failures or errors at any time. Therefore, software developers and engineers spend a lot of time and effort in order to find possible defects. This paper proposes an automatic approach to predict software defects based on machine learning algorithms. A set of complexity measures values are used to train the classifier. Three public datasets were used to evaluate the ability of mining complexity measures for different software projects to predict possible defects. Experimental results showed that it is possible to min software complexity to build a defect prediction model with a high accuracy rate.

Keywords: software defects; defect prediction; software metrics; machine learning; complexity; neural networks; naïve Bayes; decision trees; SVM; support vector machine.

DOI: 10.1504/IJCSM.2021.10040983

International Journal of Computing Science and Mathematics, 2021 Vol.13 No.4, pp.401 - 412

Received: 21 Jan 2020
Accepted: 16 Mar 2020
Published online: 15 Sep 2021 *

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