  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.
