Modified Firefly algorithm for Optimal Test Case Selection

The use of software is ubiquitous in all domains, thereby ascertaining that software testing is vital in the product development life cycle. At the outset, system performance with negligible faults in a software becomes the key aspect during the testing activity. Literature reveals that metaheuristic approaches optimize a problem by iterating, in order to improve the solution space with few or no assumptions. With research revealing that Firefly Algorithm uses swarm intelligence based approach, the proposed work focuses on selecting the test cases representing the test paths generated using Control Flow Graph. The test cases based on the brightness of the Fireflies as fitness function, including the ones at the nearest hop are selected to thereby construct an optimized test suite for three different case studies under experimentation. This work is then compared with the Artificial Bee Colony algorithm where the fitness function is the nectar amount at the closest hop.