In a critical software system, the testers have to spend an enormous amount of time and effort maintaining the software due to the continuous occurrence of defects. To reduce the time and effort of a tester, prior works in the literature are limited to using documented defect reports to automatically predict the severity of the defective software modules. In contrast, in this work, we propose a metric-based software defect severity prediction (SDSP) model that is built using a decision-tree incorporated self-training semi-supervised learning approach to classify the severity of the defective software modules. Empirical analysis of the proposed model on the AEEEM datasets suggests using the proposed approach as it successfully assigns suitable severity class labels to the unlabelled modules. On the other hand, numerous research studies have addressed the methodological aspects of SDSP models, but the gap in estimating the performance of a developed prediction using suitable measures remains unattempt. For this, we propose the risk factor, per cent of the saved budget, loss in the saved budget, per cent of remaining edits, per cent of remaining edits, remaining service time, and gratuitous service time, to interpret the predictions in terms of project objectives. Empirical analysis of the proposed approach shows the benefit of using the proposed measures in addition to the traditional measures.