Abstract
The goal set by software developers is to develop high quality and reliable software products. During the past decades, software has become complex, and thus, it is difficult to develop stable software products. Software failures often cause serious social or economic losses, and therefore, software reliability is considered important. Software reliability growth models (SRGMs) have been used to estimate software reliability. In this work, we introduce a new software reliability model and compare it with several non-homogeneous Poisson process (NHPP) models. In addition, we compare the goodness of fit for existing SRGMs using actual data sets based on eight criteria. The results allow us to determine which model is optimal.
Highlights
The basic goal set by software developers is to develop high quality software products that are stable and reliable
Most Software reliability growth models (SRGMs) are based on a non-homogeneous Poisson process (NHPP)
Li et al [12] performed new testing coverage modeling that considers the error generation and the fault removal efficiency based on NHPP
Summary
The basic goal set by software developers is to develop high quality software products that are stable and reliable. After developing a software reliability model, we fit the model to the actual data and estimate its goodness of fit. It is crucial to consider the development of a model that reflects diverse environmental factors and one that presents the best goodness of fit for actual data sets. Inoue et al [11] conducted software reliability modeling considering the uncertainty of the testing environment. Li et al [12] performed new testing coverage modeling that considers the error generation and the fault removal efficiency based on NHPP. We propose a new NHPP SRGM based on the Weibull distribution, which considers testing time with syntax error.
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