Swarm Intelligence Algorithms for Optimizing the Parameter Estimation of the NHPP Class of Software Reliability Modelling

Open-source software is gaining popularity in industrial projects due to its accessibility and cost-effectiveness. However, concerns persist about its quality and reliability. To assess software reliability quantitatively, software reliability models are utilized, with the unknown parameters of these models typically determined using statistical techniques. In many cases, these methods fail to converge to the global optimal solution of parameter estimation of nonlinear mathematical models and are quite sensitive to the initial guesses of unknown parameters. This necessitates employing a high-quality parameter estimation technique. The study demonstrates the potential application of nine nature-inspired swarm intelligence-based algorithms to address nonlinear parameter estimation problems and effectively identify the global optimal solution with high likelihood, irrespective of the initial guess. These typical algorithms are classified into several categories, including animal-inspired algorithms such as grey wolf optimizer, insect-inspired algorithms such as artificial bee colony, social spider optimization, firefly algorithm, and moth flame optimization, bird-inspired algorithms such as particle swarm optimization, sea creature-inspired algorithms such as whale optimization algorithm, and plant-inspired algorithms such as flower pollination algorithm and dandelion optimizer. Three real-world, open-source reliability datasets are utilized to assess the efficacy of these algorithms in estimating the parameters of two prominent non-homogeneous Poisson process models in software reliability.