Random forest

Ensemble learning

Support vector machine

Operating system

Classifier (UML)

Data mining

Computer science

Software quality

Software

Software metric

Software bug

Artificial intelligence

Decision tree

Schedule

Programming language

Software development

Machine learning

Journal of Software Engineering and Applications

An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. 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. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.

Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study