الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Finding, locating, and resolving software defects takes a lot of time and effort on the part of software engineers. Manual testing are required to search and analyses data in traditional testing. Manual testing are prone to making incorrect assumptions, resulting in distorted results, which leads to defects being undetected. Machine learning enables systems to learn and use what they have learnt in the future, providing software testers with more accurate information. Several advanced machine learning approaches, such as deep learning, are capable of performing a variety of software engineering tasks, including code completion, defect prediction, bug localization, clone detection, code search, and learning API sequences. One of the most essential methods of examining software quality assurance is software testing. This procedure is time-consuming and costly, accounting for over half of the total cost of software development. We’re looking to use automated methods to reduce the cost and time of the test, in addition to the cost issue. The nature of the test, which looks for errors in the programs, is such that software engineers aren’t interested in doing it, so we’re looking to use automated methods to reduce the cost and time of the test. We conducted a survey with comparison between Machine Learning, Artificial Intelligence and Data Mining algorithms that can used in Software Testing such as: Hill-Climbing Algorithm (HCA), Artificial Bee Colony Algorithm (ABC), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Harris Hawks Optimization Algorithm (HHO), Genetic Algorithm (GA), Ant Colony
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Optimization (ACO), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Hybrid Algorithms.
All the previous research talked about algorithms without using dataset or found better accuracy between them. This paper presents a comparisons of classification algorithms such as: Logistic Regression, K_Neighbors, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Gaussian NB, Support Vector Machine and deep learning algorithms such as: Artificial Neural Network, Convolutional Neural Network. We have employed PROMISE (CM1: 498 observations, KC1: 2109 observations, KC2: 520 observations, PC1: 1109 observations) data from the directory of NASA to conduct our research. We split the dataset into training dataset (70%) and testing datasets (30%). We brought up a machine learning hybrid approach by Particle Swarm Optimization (PSO) and Artificial neural networks (ANN) to overcome the ongoing problem. The proposed model also presents higher evaluation in the terms of other criteria, reduce time and space complexities.
The experimental results revealed that PSO-ANN to overcome the problem of the curse of dimensionality and reduce the computational requirements of the proposed task. It was the major problem with the previous research methodology mentioned then after we used PSO with multi-layer (ANN), which is a very powerful methodology of classification in ML. we have found better accuracy (CM1: 91.0%, KC1: 86.0%, PC1:94.0%) than other methods.