Constraints handling in combinatorial interaction testing using multi-objective crow search and fruitfly optimization

Combinatorial testing strategies are the recent interest of the researchers because of their wide variety of applications. The combinatorial testing strategy posses a great deal of minimizing the count of the input parameters of a system such that a small set of parameters is obtained depending on their interaction. Practically, the input models of the software system are subjected to the constraints mainly in highly configurable systems. There exist a number of issues while integrating the constraint in the testing strategy that is overcome using the proposed method. The proposed method aims at developing the combinatorial interaction test suites in the presence of constraints. The proposed strategy is multi-objective crow search and fruitfly optimization that is developed by the integration of the crow search algorithm and the chaotic fruitfly optimization algorithm. The proposed algorithm offers an optimal selection of the test suites at the better convergence. The experimentation based on the constraints and the analysis are carried out in terms of average size and average time with their values as 10 and 30 s, respectively.