Efficient Multiple Path Coverage in Mutation Testing with Fuzzy Clustering-Integrated MF_CNNpro_PSO

Fault concealment in complex software programs and the difficulty of generating test cases to detect such faults present significant challenges in software testing. To resolve these challenges, this paper suggests a novel method that integrates mutation testing, fuzzy clustering, convolutional neural networks (CNN), and particle swarm optimization (PSO) to efficiently generate test cases that cover multiple paths with numerous faults (mutant branches). Initially, mutation-based paths are classified using fuzzy clustering based on their coverage difficulty and similarity. A multi-feature CNN model (MF_CNNpro) is then constructed and trained on the paths of each cluster. Finally, the predicted particles from the MF_CNNpro model are used as the initial population for PSO, which evolves to generate the test cases. The proposed method is evaluated on six test programs, and the results demonstrate that it significantly improves clustering separation and reduces clustering compactness. By selecting only the cluster center paths to construct the MF_CNNpro model, training and prediction costs are effectively reduced. Moreover, the use of MF_CNNpro and PSO to select representative individuals as the initial population greatly enhances the evolutionary efficiency of PSO. The proposed method outperforms traditional approaches in clustering, prediction, and test data generation. Specifically, the SC clustering method improves cluster separation (SP) by 0.021, reduces compactness (CP) by 0.054, and decreases clustering rate (CR) by 4.97%, thereby enhancing clustering precision. The MF_CNNpro model improves the IA metric by 38.2% and reduces the U-Statistic and MSE by 83.0% and 97.9%, respectively, optimizing prediction performance. The MF_CNNpro+PPSOpro method increases the path coverage success rate from 47.9% to 97.4% (a 103.3% improvement), reduces the number of iterations by 84.1%, and decreases execution time by 95.6%, significantly improving generation efficiency.