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An Approach To Extract Optimal Test Cases Using AI
رویکردی برای استخراج موارد آزمایش بهینه با استفاده از هوش مصنوعی-2020 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 natureinspired
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. Keywords: Software testing | Regression testing | Optimization | Harris Hawks Optimization | Test case selection |
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