A Multi-Objective optimization algorithm for uniformly distributed generation of test cases

Multi-objective optimization deals with conflicting objectives. A multi-objective problem is determined to find acceptable solution for all conflicting objectives based on the concept of Pareto-Optimality. This paper focuses automatic test data generation on the aspect of Multi - Objective. One objective will be uniformly distribution and another is to maximize code. Multi-objective optimization incorporates decision making. This paper also covers non-dominance property to maintain sub-population of best fitness value. Generally, multi-objective optimization has two approaches, one is to decompose multi-objective into various single objective components, and another is to evolve Pareto-optimal set of solutions. Software Testing is a tedious, critical and expensive phase of software development, thus there is a need for automatic generation of uniformly distributed test cases over the provided range with maximum code coverage. Software system failure incur huge loss, to overcome this problem an efficient testing approach is required.