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International audienceIn the context of Object-Oriented software, manyworks have investigated the Class Integration and Test Order (CITO)problem, proposing solutions to determine test orders for theintegration test of the program classes. The existing approaches basedon graphs can generate solutions that are sub-optimal, and do notconsider the different factors and measures that can affect the stubbingprocess. To overcome this limitation, solutions based on GeneticAlgorithms (GA) have presented promising results. However, thedetermination of a cost function, which is able to generate the bestsolutions, is not always a trivial task, mainly for complex systems witha great number of measures. Therefore, we introduce, in this paper, amulti-objective optimization approach to better represent the CITOproblem. The approach generates a set of good solutions that achieve abalanced compromise between the different measures (objectives). It wasimplemented by a Pareto Ant Colony (P-ACO) algorithm, which is describedin detail. The algorithm was used in a set of real programs and theobtained results are compared to the GA results. The results allowdiscussing the difference between single and multi-objective approachesespecially for complex systems with a greater number of dependenciesamong the classes

A Pareto Ant Colony Algorithm Applied to the Class Integration and Test Order Problem