Novel multi objective test case selection framework for object oriented software using hybrid bat algorithm based approach

Abstract

Software testing is conducted to ensure the accuracy and reliability of newlinethe software. Test case selection for class testing when generating a collection of newlinetest data from class specifications to ensure the proper functioning of class newlineimplementations is the most important problem in the field of object-oriented newlinetesting. Traditional research methods must be tested in order to decide whether newlinenew techniques still need to be built in relation to object-oriented systems. Time newlineand resources are usually neglected areas in the life cycle of software newlinedevelopment. So, these become the primary constraints in software testing. newlineOptimization of a test suite is crucial in reducing the complexity of the testing newlinephase and selecting the test cases by eliminating redundant data; this is critical newlinefor defining the strategies. Most of the work in literature employs singleobjective newlineoptimization approaches. Though these are not always efficient, they newlineplay a critical role in selecting a test case. newlineThe Greedy algorithms are very effective in a single objective newlineproblem formulation. To optimize the cost and coverage, it will have to be newlinedevised for measuring code and time. This results in the production of one single newlineobjective cost function and the cognizant variant of that of the greedy algorithm newlinewas extensively applied for the problem of a single objective optimization newlineproblem. Since test case selection is non-deterministic, the scope of this research newlinework focuses on a novel approach to reduce the number of test cases. A novel newlinemulti-objective BAT algorithm that uses code coverage and execution cost as the newlineobjective function is proposed in this work for the selection of a test cases for newlineobject-oriented testing. Non-Dominated Sorting Genetic Algorithm (NSGA) is a newlineMulti Objective Optimization (MOO) algorithm. newline