An Improved Crow Search Algorithm for Test Data Generation Using Search-Based Mutation Testing

Automation of test data generation is of prime importance in software testing because of the high cost and time incurred in manual testing. This paper proposes an Improved Crow Search Algorithm (ICSA) to automate the generation of test suites using the concept of mutation testing by simulating the intelligent behaviour of crows and Cauchy distribution. The Crow Search Algorithm suffers from the problem of search solutions getting trapped into the local search. The ICSA attempts to enhance the exploration capabilities of the metaheuristic algorithm by utilizing the concept of Cauchy random number. The concept of Mutation Sensitivity Testing has been used for defining the fitness function for the search based approach. The fitness function used, aids in finding optimal test suite which can achieve high detection score for the Program Under Test. The empirical evaluation of the proposed approach with other popular meta-heuristics, prove the effectiveness of ICSA for test suite generation using the concepts of mutation testing.