Performance analysis of software reliability growth models with testing-effort and change-point

TL;DR: Experimental results show that the proposed framework to incorporate both testing-effort and change-point for SRGM has a fairly accurate prediction capability.

Abstract: In this paper, a scheme for constructing software reliability growth model based on Non-Homogeneous Poisson Process is proposed. The main focus is to provide a method for software reliability modeling, which considers both testing-effort and change-point. In the vast literature, most researchers assume a constant detection rate per fault in deriving their software reliability models. They suppose that all faults have equal probability of being detected during the software testing process, and the rate remains constant over the intervals between fault occurrences. In reality, the fault detection rate strongly depends on the skill of test teams, program size, and software testability. Therefore, it may not be smooth and can be changed. On the other hand, sometimes we have to detect more additional faults in order to reach the desired reliability objective during testing. It is advisable for project managers to purchase new automated test tool, technology or additional manpower. These approaches can provide a conspicuous improvement in software testing and productivity. In this case, the fault detection rate will be changed during the software development process. Therefore, here we incorporate both generalized logistic testing-effort function and change-point parameter into software reliability modeling. New theorems are proposed and software testing data collected from real application are utilized to illustrate the proposed model. Experimental results show that the proposed framework to incorporate both testing-effort and change-point for SRGM has a fairly accurate prediction capability.