An Efficient Early Software Reliability Prediction using Particle Swarm Optimization (PSO)
P. Ashwini, B. Rajani, B. Vijitha · 2024
Software testing is very costly and time-consuming, and nearly half of the expenditures associated with software development go toward testing. Software fault prediction is the process of creating modules to help developers in detecting failures in project modules. The process of creating a collection of data for software testing based on a certain criterion is known as test data creation. Particle Swarm Optimization (PSO) uses an iterative method to optimize a solution to the problem. The basic component that affects how the software testing process is modeled to create the predicted faults is the correlation between execution time and the failure count. In this study, we implemented Particle Swarm Optimization (PSO) technique’s initial conceptualization as a means of addressing the issue of software reliability growth modelling. The Goel-Okumoto, MusaOkumoto, Delayed S-Shaped, and Power reliability growth models’ parameters have all been estimated using the suggested technique. The estimated parameters were further used for decision making, such as, remaining faults in the software, future testing time and time to market for software product.