Towards Developing and Analysing Metric-Based Software Defect Severity Prediction Model

In a critical software system, the testers have to spend an enormous amount of time and effort to maintain the software due to the continuous occurrence of defects. Among such defects, some severe defects may adversely affect the software. To reduce the time and effort of a tester, many machine learning models have been proposed in the literature, which use the documented defect reports to automatically predict the severity of the defective software modules. In contrast to the traditional approaches, in this work we propose a metric-based software defect severity prediction (SDSP) model that uses a self-training semi-supervised learning approach to classify the severity of the defective software modules. The approach is constructed on a mixture of unlabelled and labelled defect severity data. The self-training works on the basis of a decision tree classifier to assign the pseudo-class labels to the unlabelled instances. The predictions are promising since the self-training successfully assigns the suitable class labels to the unlabelled instances. On the other hand, numerous research studies have covered proposing prediction approaches as well as the methodological aspects of defect severity prediction models, the gap in estimating project attributes from the prediction model remains unresolved. To bridge the gap, we propose five project specific measures such as the Risk-Factor (RF), the Percent of Saved Budget (PSB), the Loss in the Saved Budget (LSB), the Remaining Service Time (RST) and Gratuitous Service Time (GST) to capture project outcomes from the predictions. Similar to the traditional measures, these measures are also calculated from the observed confusion matrix. These measures are used to analyse the impact that the prediction model has on the software project.