DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-12,Issue-1 ( Jan, 2024 ) | |||||||||
Paper Title |
Software Testing Automation Using Machine Learning Techniques | |||||||||
Author Name |
Mustafa Abdul Salam, Mohamed Abdul-Fattah, Abdullah Mohamed | |||||||||
Affilition |
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Pages |
1-15 | |||||||||
Abstract |
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. Keywords - Software testing automation, classification algorithms, deep learning, particle swarm optimization | |||||||||
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