Applications of different metaheuristic techniques for finding optimal tst order during integration testing of object oriented systems and their comparative study
This work addresses the CITO problem for software testers, but it is incremental as it applies a known metaheuristic to a specific domain.
The paper tackled the class integration test order (CITO) problem in object-oriented systems by proposing a genetic algorithm-based approach that models dependencies using a weighted class dependency graph to minimize stub complexity, and it compared empirical results with existing techniques.
In recent past, a number of researchers have proposed genetic algorithm (GA) based strategies for finding optimal test order while minimizing the stub complexity during integration testing. Even though, metaheuristic algorithms have a wide variety of use in various medium to large size optimization problems [21], their application to solve the class integration test order (CITO) problem [12] has not been investigated. In this research paper, we propose to find a solution to CITO problem by the use of a GA based approach. We have proposed a class dependency graph (CDG) to model dependencies namely, association, aggregation, composition and inheritance between classes of unified modeling language (UML) class diagram. In our approach, weights are assigned to the edges connecting nodes of CDG and then these weights are used to model the cost of stubbing. Finally, we compare and discuss the empirical results of applying our approach with existing graph based and metaheuristic techniques to the CITO problem and highlight the relative merits and demerits of the various techniques.