Machine Learning Approach for Regression Testing: A Case Study in Markov Chain Model - Rawat, Neelam; Somani, Vikas; Tripathi, Arun Kr. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In the wake of digitization of business processes, Software has turned into an approach to carrying out the businesses while considering software testing as an investment not as an expense. Now-adays, businesses focus on prioritizing scalability so that they may allow testers to make con-tinuous integration and testing without affecting end user’s experience towards the application. Regression Testing can be performed multiple times (if required) to either to whole application under test or to only part of the application to ensure the validity of the changes made. Because of the repetitive nature of Regression Testing, software testers need to automate the process to minimize the consumption of time and effort. This paper mainly focuses on one of the machine learning techniques i.e., Markov Chain Model that randomly selecting a path to generate test cases that may further identify those test cases which are most prospective to uncover the new bugs. This can be done by observing the transition from a state in which no bugs have been found to a state in which a bug has been found.