Estimating Software Reliability with Static Project Data in Incremental Development Processes

Incremental development of software becomes much popular and enables to reduce the development cost effectively. On the other hand, it has not been known yet that the incremental development can really contribute to guarantee the software reliability more than the waterfall development paradigm. In this paper we estimate quantitative software reliability with both of static fault count data and static metrics data for incremental development processes. Since the measurement of software development project data is often expensive, we encounter the situation where the time series data are not always available. We develop metrics-based software reliability models based on the non-homogeneous Poisson processes for the purpose of reliability assessment in the incremental development, and compare them with an elementary approach with multiple linear regression model. Numerical examples are given with real software project data to show that our proposed methods outperform the common multiple linear regression model under the assumption on independent incremental testing phases.