Software reliability analysis and assessment using queueing models with multiple change-points

AbstractOver the past three decades, many software reliability growth models (SRGMs) have been proposed, and they can be used to predict and estimate software reliability. One common assumption of these conventional SRGMs is to assume that detected faults will be removed immediately. In reality, this assumption may not be reasonable and may not always occur. During debugging, developers need time to reproduce the failure, identify the root causes of faults, fix them, and then re-run the software. From some experiments or observations, the fault correction rate may not be a constant and could be changed at certain points as time proceeds. Consequently, in this paper, we will investigate and study how to apply queueing models to describe the fault detection and correction processes during software development. We propose an extended infinite server queueing model with multiple change-points to predict and assess software reliability. Experimental results based on real failure data show that the proposed model can depict the change of fault correction rates and predict the behavior of software development more accurately than traditional SRGMs