Parallel Computing-Driven Genetic Algorithms and Particle Swarm Optimization for Enhanced Software Testing Efficiency

Test software in highly dynamic and complex environments, where it becomes increasingly difficult with rising computational costs and execution times, and to accurately detect faults. Traditional testing methods are not enough in handling even modest-sized automated or manual testing in most cases, when such methods are over gunned and blown-out by the underlying complexity of a large-scale, complex software system. This paper describes a Hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)-based software testing framework, one that takes parallel computing techniques for addressing the problem. Heterogeneous testing could be then applied to the optimization of test cases: selection, prioritization, and fault detection. It combines with fast convergence speed of PSO and exploration power of GA. To improve testing efficiency by reducing the execution time while still ensuring high precision and fault detection coverage, the parallel computing methods such as CPU multithreading and GPU acceleration, or even distributed computing, are utilized. This study evaluates the hybrid GA-PSO model and considers its performance with respect to traditional methods like GA and PSO with the following key metrics: accuracy, execution time, test case diversity, and fault detection rate. The framework improves testing efficiency significantly by 20.8% in execution time and 4.4% in software coverage. The enhanced fault detection function also demonstrates the model's effectiveness in identifying defects more accurately and efficiently. The GA-PSO hybrid model is particularly suitable for mass-scale automated software testing environments where time and resource optimization are of the highest priority. Parallel computing significantly accelerates the optimization process, allowing for faster identification of major bugs and overall software reliability enhancement. With this approach, not only is the efficiency of software testing enhanced, but the testing process is also capable of handling complicated software systems with higher scalability and efficiency. In summary, the proposed GA-PSO hybrid model, in combination with parallel computing, is an outstanding contribution to the optimization of software testing processes, minimization of execution time, and enhancement of defect detection, and hence is an effective tool for today's software testing challenges