Software Reliability Prediction and Analysis Using Queueing Models with Multiple Change-Points

Abstract

Over the past three decades, many software reliability growth models (SRGMs) were proposed and they are aimed at predicting and estimating software reliability. One common assumption of these conventional SRGMs is that detected faults will be removed immediately. In reality, this assumption may not be reasonable and may not always occur. Developers need time to identify the root causes of detected faults and then fix them. Besides, during debugging the fault correction rate may not be a constant and could be changed at some certain points as time proceeds. Consequently, in this paper, we will explore and study how to apply queueing model to investigate the fault correction process 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 proposed model can depicts the change of fault correction rates and predict the behavior of software development more accurately than traditional SRGMs.