Abstract
The problem known as CAITO refers to the determination of an order to integrate and test classes and aspects that minimizes stubbing costs. Such problem is NP-hard and to solve it efficiently, search based algorithms have been used, mainly evolutionary ones. However, the problem is very complex since it involves different factors that may influence the stubbing process, such as complexity measures, contractual issues and so on. These factors are usually in conflict and different possible solutions for the problem exist. To deal properly with this problem, this work explores the use of multi-objective optimization algorithms. The paper presents results from the application of two evolutionary algorithms - NSGA-II and SPEA2 - to the CAITO problem in four real systems, implemented in AspectJ. Both multi-objective algorithms are evaluated and compared with the traditional Tarjan's algorithm and with a mono-objective genetic algorithm. Moreover, it is shown how the tester can use the found solutions, according to the test goals.
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