combinatorial testing (ct)

The need to increase the use of Combinatorial Testing (CT) in software testing has become a necessity in software development. CT is an efficient approach to reduce the size of the test suite so that the software can be tested with fewer test cases. Covering Array (CA) is one of the important branches in CT, which has different types. Many solutions have been provided for its production, which have appropriate efficiency (array size) and performance (speed). But there is a lack of a solution that has both efficiency and performance. In this research, we have tried to produce an optimized test suite(with the minimum number of test cases) by using the gravitational search algorithm(GSA) and changing the neighbor selection method. Also, by changing the structure of the data and giving weight to the parameters not covered, we have increased the speed of producing the test suite. The weighting of non-covered parameters and the change in the behavior of the gravity algorithm have caused a smart search to find non-covered test cases. This increase in speed has made the proposed solution capable of producing test suites for high-power configurations. Also, the evaluation results show that the proposed solution outperforms other popular algorithms such as the genetic algorithm, the particle mass search algorithm, and even the gravity search algorithm itself.