Software Fault Prediction has a critical role in improving effectiveness and reliability of software systems by identifying potential faults early in the development cycle. A hybrid PSO optimized CNN-RNN model leverages the strengths of both RNN and CNN in capturing temporal and spatial data features, while PSO optimizes hyperparameters to enhance model performance in this research. The proposed PSO optimized CNN-RNN model is compared against existing hybrid machine learning models, where PSO optimized Genetic Algorithm (GA) was used for hyperparameter tuning and feature selection of Support Vector Machine (SVM). Our experiments are performed on publicly available software fault’s datasets, providing a comprehensive comparison of model performance that is evaluated on the basis of various matrices of performance like F-measures, accuracy, recall, F1-score, SD and precision. The results demonstrate that while optimized Machine Learning algorithms perform well in some cases, the CNN-RNN-PSO model consistently outperforms them, offering superior fault prediction capabilities. The NASA MDP repository’s benchmark datasets are used for the comparative analysis and the results demonstrated that the optimized hybrid machine learning model achieves competitive performance. The proposed PSO optimized CNN-RNN model demonstrates superior accuracy and robustness due to its deep learning architecture and optimization capabilities. This research focus on the potential of a hybrid DL approach which improves the software reliability and suggests future directions for integrating intelligent models in SFP.

Keywords： convolutional neural network ; software fault prediction ; particle swarm optimization ; deep learning ; recurrent neural network ; machine learning