Article citationsMore>>

Menzies, T., Greenwald, J. and Frank, A. (2007) Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering, 33, 2-13.
https://doi.org/10.1109/TSE.2007.256941

has been cited by the following article:

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

  AUTHORS: Abdullah Alsaeedi, Mohammad Zubair Khan

  KEYWORDS: Machine Learning, Ensembles, Prediction, Software Metrics, Software Defect

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.12 No.5, May 21, 2019

  ABSTRACT: 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.