Soft Hunting Algorithm for Auto-Tuning Software Reliability Growth Models

Abstract

Nature-inspired algorithms are popular for auto-tuning software reliability growth models in recent decades due to their derivative-free natural tendency to circumvent the local optima problem. These methods have indeed exhibited enormous effectiveness in estimating software dependability. The goal of this study is to present a new nature-inspired approach for parameter improvement of system reliability predictions based on the hunting abilities of smoother-coated otters. The otters’ most notable characteristic is their ability to hunt in groups. In this study, clever otter hunting behavior is used to enhance reliability engineering parameters of the model. Otters work well together and have a strong sense of teamwork. Matte finish coated otters’ smart fish scavenging capacity distinguishes them from many other swarm intelligence-based techniques. Three stages of otter searching are accomplished: traveling in a V formation in the direction of the prey’s movements, advancing forcefully via the stream, and then assaulting the prey on the beach. Three software dependability models are utilized to validate the applicability of the suggested approach. The study’s findings demonstrate that the suggested algorithm outperformed the ABC, GA, and PSO algorithms by 75% and 50%, respectively, in terms of reduced SSE and lower MSE. The smooth-coated otter’s cognitive foraging behavior gives great gain capabilities in parameters software cost estimation system reliability analysis. The outcomes are encouraging for auto-tuning software reliability growth models. Smooth-covered otter optimization can also be used to solve other efficiency challenges.