Applying Genetic Algorithm for Prioritization of Test Case Scenarios Derived from UML Diagrams
This work addresses software testing efficiency for developers, but it is incremental as it applies existing methods like genetic algorithms to a specific domain.
The paper tackles the problem of time-consuming exhaustive software testing by proposing a technique to prioritize test case scenarios derived from UML diagrams using a genetic algorithm, resulting in optimized testing efficiency.
Software testing involves identifying the test cases whichdiscover errors in the program. However, exhaustive testing ofsoftware is very time consuming. In this paper, a technique isproposed 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.