Analysis of a software reliability growth model with logistic testing-effort function

We investigate a software reliability growth model (SRGM) based on the Non Homogeneous Poisson Process (NHPP) which incorporates a logistic testing effort function. Software reliability growth models proposed in the literature incorporate the amount of testing effort spent on software testing which can be described by an exponential curve, a Rayleigh curve, or a Weibull curve. However it may not be reasonable to represent the consumption curve for testing effort only by an exponential, a Rayleigh or a Weibull curve in various software development environments. Therefore, we show that a logistic testing effort function can be expressed as a software development/test effort curve and give a reasonable predictive capability for the real failure data. Parameters are estimated and experiments on three actual test/debug data sets are illustrated. The results show that the software reliability growth model with logistic testing effort function can estimate the number of initial faults better than the model with Weibull type consumption curve. In addition, the optimal release policy of this model based on cost reliability criterion is discussed.