Improving the Accuracy of Software Reliability Modeling by Predicting the Number of Secondary Software Defects

Reliability assessment and prediction of the number of faults/defects is an important part of the software engineering process. Many software reliability models assume that all detected are removed with certainty and no new faults are introduced. However, the introduction of secondary faults during software updates has become quite common in software development practice, which can be explained by the enormous complexity of modern computer applications. In the paper we consider different scenarios of introducing secondary faults and how to predict number of such faults. Finally, we discuss how different SRGMs like Jelinski-Moranda, Exponential, Schick-Wolverton, Musa and Lipov models can be modified to account secondary faults in order to improve accuracy of software reliability prediction. We use an industrial case study to demonstrate applicability of the proposed approach. Our results show that considering secondary faults helped to considerably improve accuracy of software failure rate prediction