Test Case Optimization using Meta-Heuristic Algorithm

Abstract

There are a variety of software engineering areas. Testing is considered as one of the important process in software development, which is still highly unreliable, costly and ad-hoc. It is one of the most difficult and time-consuming tasks where a number of efforts may be required to run all variations of test cases in the test suite. Software testingissues which is viewed as an optimization problem can be overcome by using various optimization methods. To diagnose the flaws of the code, research needs all possible test cases. Regression testing is used to update software, which revalidates the original application device functionality. A research suggests that by optimizing multiple objects they can be evaluated in their best positions by usingparticle swarm optimization technique.Techniques to prioritize test cases may take advantage of test cases and try to improve regression testing efficacy. This paper intends to use the particle swarm optimization (PSO) algorithm to classify test cases on the basis of the device units updated automatically. This is targeted at prioritizing test cases similar to the newest order utilizing modified program elements, so that test cases of multiple and greater significance may be identified through regression testing.The empirical results demonstrate that the test cases can be prioritized effectively and reliably in the test suites using the PSO algorithm.