Software Quality Prediction Using Machine Learning Techniques

Abstract

AbstractSoftware quality prediction is one of the most challenging tasks in the development and maintenance of the software. Machine learning (ML) is widely being incorporated for the prediction of the quality of final product in the early development stages of SDLC. ML prediction model is set using software metrics and faulty data of previous projects to detect the high-risk modules for future projects so that the testing efforts can be targeted to those specific ‘risky’ modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexpensively and ensure the timely delivery of a successful, failure-free and supreme quality software product within budget. This paper brings a comparison of 30 software quality prediction models built on five ML techniques such as artificial neural network and support vector machine, decision tree, k-nearest neighbor and Naïve-Bayes classifiers using six datasets. These models exploit the predictive power of static code metrics—McCabe complexity metrics—for the quality prediction. All thirty predictors are compared using ROC, AUC and accuracy as performance evaluation criteria. The results show that ANN technique is the most promising for accurate quality prediction irrespective of the dataset used.KeywordsANNClassification treeMachine learningSoftware quality