An Approach To Extract Optimal Test Cases Using AI

Regression testing is the backbone of the functional Software Testing. Unlike any other testing; regression validation evolves the whole suite of code which incorporates the existing code as well as new code or the change request. Validating all the possible scenarios is not effective as it increases the expenditure. This gains the outlook for the researchers to analyze a more efficient way for regression testing by electing a subset from the test suite to spot the defects. Ample research has crop up for this NP-Hard problem and folks are implementing the metaheuristic techniques and dominantly the nature-inspired ones. In this paper, to extract the optimal test cases we have utilized Harris Hawks Optimization (HHO) which is a nature-inspired technique and portrays chasing drive away style of Harris' hawks termed as Surprise Pounce. In this tactic, assorted hawks combine together to pounce a prey through the offbeat directions to surprise the prey. This paper focuses on the Harris Hawks Optimization algorithm and its applications in the domain of software testing.