Applying Genetic Algorithm for Prioritization of Test Case Scenarios Derived from UML Diagrams

Software testing involves identifying the test cases which discover errors in the program. However, exhaustive testing of software is very time consuming. In this paper, a technique is proposed to prioritize test case scenarios by identifying the critical path clusters using genetic algorithm. The test case scenarios are derived from the UML activity diagram and state chart diagram. The testing efficiency is optimized by applying the genetic algorithm on the test data. The information flow metric is adopted in this work for calculating the information flow complexity associated with each node of the activity diagram and state chart diagram. If the software requirements change, the software needs to be modified and this requires re – testing of the software. Hence, to take care of requirements change, a stack based approach for assigning weights to the nodes of activity diagram and state chart diagram has also been proposed. In this paper, we have extended our previous work of generating test case scenarios from activity diagram by also considering the concurrent activities in nested activity diagram.