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A Survey on Software Testing Automation using Machine Learning Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Mustafa Abdul Salam, Mohamed Abdel-Fattah, Abdullah Abdel Moemen

Mustafa Abdul Salam, Mohamed Abdel-Fattah and Abdullah Abdel Moemen. A Survey on Software Testing Automation using Machine Learning Techniques. International Journal of Computer Applications 183(51):12-19, February 2022. BibTeX

	author = {Mustafa Abdul Salam and Mohamed Abdel-Fattah and Abdullah Abdel Moemen},
	title = {A Survey on Software Testing Automation using Machine Learning Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2022},
	volume = {183},
	number = {51},
	month = {Feb},
	year = {2022},
	issn = {0975-8887},
	pages = {12-19},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2022921919},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Finding, locating, and resolving software defects takes a lot of time and effort on the part of software engineers. Humans are required to search and analyses data in traditional testing. Humans 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. Researchers are looking for using automated methods to reduce the cost and time of the test, in addition to the cost issue. A survey has been conducted with comparison between Machine Learning, and Data Mining algorithms.These algorithms are such as: Hill-Climbing Algorithm (HCA), Artificial Bee Colony Algorithm (ABC), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Artificial Bee Colony Algorithm (ABC), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Hybrid Algorithms.


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Machine learning, artificial intelligence, data mining, Software Testing, Machine Learning, Testing Automation, Software Testing Tool.