Finding, locating, and resolving software defects takes a lot of time and effort. This paper proposes a hybrid
machine learning model to automate the software testing process. The proposed model combines particle swarm
optimization (PSO) to optimize artificial neural network (ANN) to overcome the local minima and overfitting problems. The
proposed model is compared with different classification algorithms such as: Logistic Regression, K nearest neighbours
(KNN), Decision Tree, Random Forest, Gradient Boosting, AdaBoost, Linear Discriminant Analysis, Quadratic
Discriminant Analysis, Gaussian NB, Support Vector Machine and deep learning neural networks. The effectiveness of the
proposed model is evaluated using four different datasets (CM1, KC1, KC2, and PC1). Datasets have been divided into
training part (70%) and testing part (30%). The proposed model achieved higher accuracy than compared algorithms, while
also reducing time and space complexities. |
