Predicting Defect Prone Modules in Web Applications

Abstract

Predicting defect proneness of software products has been an active research area in software engineering domain in recent years. Researchers have been using static code metrics, code churn metrics, developer networks, and module networks as inputs to their proposed models until now. However, domain specific characteristics of software has not been taken into account. In this research, we propose to include a new set of metrics to improve defect prediction performance for web applications by utilizing their characteristics. To validate our hypotheses we used datasets from 3 open source web applications to conduct our experiments. Defect prediction is then performed using different machine learning algorithms. The results of experiments revealed that overall performance of defect predictors are improved compared to only using existing static code metrics. Therefore we recommend practitioners to utilise domain-specific characteristics in defect prediction as they can be informative.