Genetic Algorithms for Superior Program Path Coverage in software testing related to Big Data

Abstract

By using advanced genetic algorithms (GAs) to maximize test data production and path coverage, this work improves software testing. Whereas hybrid algorithms integrate GAs with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), adaptive mechanisms in real- time alter algorithm parameters. Co-evolutionary techniques address test efficiency, coverage, and computing overhead by simultaneously evolving numerous subpopulations. These improvements work especially well in settings involving parallel computing and big data. Test coverage and efficiency have significantly improved in the experimental results, indicating that these advanced GAs have the potential to completely transform software testing procedures. The study emphasizes how crucial it is to build scalable and resilient software testing frameworks using adaptive, hybrid, and co-evolutionary approaches as a way to achieve improved performance and dependability in complex software systems.