A technique for early prediction of software reliability based on design metrics

In the early stages of development, it is difficult to quantitatively assess the reliability of a software product. In this context, we propose a bottom-up approach to predict the reliability of an object-oriented software from its product metrics gathered during the architectural design stage. A fault model is constructed to categorize different kinds of faults that can occur in the components making up the software product. Subsequently, the product metrics collected during the software design phase are used to estimate the expected number of different kinds of faults that may occur in a component. Eventually, these estimated values of the different kinds of faults are used to predict the expected values of the total number of faults present in the component. We use the estimated fault content of the component and the number of tests that will be performed over the component, to predict reliability of the component. We adopt a probabilistic approach, Bayesian Belief Network, for reliability prediction of the components from product metrics. Based on predicted reliabilities and usage frequencies of the components, the reliability of a system is predicted. The applicability of our proposed model is illustrated through a case study. Moreover, we performed a set of experiments and also compared our approach with an established approach reported in the literature to investigate the accuracy of our approach. Analysis of the results from our experiments suggests that our approach yields reasonably accurate result.