Parameter Estimation of Software Reliability Growth Models: A Comparison Between Grey Wolf Optimizer and Improved Grey Wolf Optimizer

摘要

In modern era, industries demand for innovative and reliable software solutions. To maintain the reliability level of softwares various software reliability growth models were proposed in last four decades. These models performance relies on parameter estimation approaches utilized to find the optimum values of their unknown model parameters. But, developing an approach that provides the perfect optimum parameter for software reliability growth models (SRGMs) has been the issue of concern within the research community over the decades. This paper adopted the Improved Grey Wolf Optimizer (IGWO) for parameter estimation and compares its accuracy with existing approach Grey Wolf Optimizer (GWO) in estimating the optimum parameters for software reliability growth models. GWO imitates the social leadership pyramid and the hunting methods adopted by grey wolves; IGWO was later proposed to resolve the deficiencies observed in GWO for improved performance. Seven real world failure datasets have been utilized to measure and evaluate the performance of the proposed approach against the existing approach. The results indicate that proposed approach (IGWO) outperform the existing one (GWO).