Analysis of a software reliability growth models: the case of log-logistic test-effort function

It is quite natural to produce reliable software systems efficiently since the breakdown of the computer systems, which is caused by software errors, results in a tremendous loss and damage for social life. We present Software Reliability Growth Model (SRGM) based on nonhomogeneous Poisson process (NHPP), which incorporates the amount of testing effort consumptions during software testing phase. The time dependent behavior of testing effort consumptions is described by Log-Logistic curve. [1] has used this model into SRGM for finite failure NHPP. In this paper, we will show that a Log-Logistic Test-Effort Function (TEF) can be expressed as a Software Development/test-effort curve. It is assume that the error detection rate to the amount of testing-effort spent during the testing phase is proportional to the current error content. The SRGM parameters are estimated by least square estimation (LSE) and Maximum likelihood Estimation (MLE) methods. The method of data analysis for software reliability measurements are presented for three real data set and results are compared with other existing models to show that the proposed model is good enough to give more accurate description of resources consumption give better fit. This model can be applied to a wide range of software system. In addition, the optimal release policy based on reliability and cost criteria for software system are proposed.