Prioritization of test case scenarios derived from activity diagram using genetic algorithm

Software testing involves identifying the test cases which discovers the errors in the program. However, the exhaustive testing is rarely impossible and very time consuming. In this paper, the software testing efficiency is optimized by identifying the critical path clusters. The test case scenarios are derived from the activity diagram and 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 activity diagram.