Generalized Logit Regression-Based Software Reliability Modeling with Metrics Data

It is well known that multifactor software reliability modeling with software metrics data is useful to predict the software reliability with higher accuracy, because it utilizes not only software fault count data but also software testing metrics data observed in the development process. In this paper we extend the existing logit regression-based software reliability model by introducing more generalized logistic type functions and improve the goodness-of-fit and predictive performances. In numerical examples with real software development project data, it is shown that our generalized models can outperform the existing logit regression-based model and the Cox regression-based model significantly.