Test case prioritization at various levels of software development life cycle

Abstract

Software testing is often regarded as the most effective method for en- suring software quality. Automated testing, in particular, is a viable and helpful way of producing test cases. In recent years, several auto- matic test generating methodologies have been published and the most sophisticated testing methods are requirements-based, model-based, and code-based testing approaches. The objective of automatic test- ing is to generate a number of qualitative test cases with satisfying testing objectives such as adequacy criteria, testing expenses, and im- prove the testing efficiency of the software products. However, these approaches are not capable of generating qualitative test cases for some complex real-life applications. This is due to the limitations of the testing tools or the user testing methodology. One of the possi- ble solutions is to use metaheuristic techniques to produce qualitative test cases to overcome such limitations. This method makes use of problem-specific data to identify a good enough solution to a specific problem. Because exhaustive testing is impossible due to the large size and complex application under test, the employment of metaheuristic search techniques for testing appears promising. Search-based testing applies metaheuristic search techniques in a variety of test case gen- eration methodologies, including white-box, black-box, and grey-box testing. Researchers have been working hard in this field to increase the efficiency of software testing. There are several test case genera- tion and prioritization methods available to achieve a high percentage of code coverage. However, based on existing findings, it is expected that further effort would be required to improve the efficiency of test- ing methods.To improve this requirement further, this thesis discusses some meta- heuristic techniques such as Particle Swarm Optimization (PSO), Ant Colony Optimization, and Chaotic Grey Wolf Optimization (CGWO) to generate and then prioritize test cases for object-oriented systems during the