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Abstract : In the context of Object-Oriented software, many
works have investigated the Class Integration and Test Order (CITO)
problem, proposing solutions to determine test orders for the
integration test of the program classes. The existing approaches based
on graphs can generate solutions that are sub-optimal, and do not
consider the different factors and measures that can affect the stubbing
process. To overcome this limitation, solutions based on Genetic
Algorithms (GA) have presented promising results. However, the
determination of a cost function, which is able to generate the best
solutions, is not always a trivial task, mainly for complex systems with
a great number of measures. Therefore, we introduce, in this paper, a
multi-objective optimization approach to better represent the CITO
problem. The approach generates a set of good solutions that achieve a
balanced compromise between the different measures (objectives). It was
implemented by a Pareto Ant Colony (P-ACO) algorithm, which is described
in detail. The algorithm was used in a set of real programs and the
obtained results are compared to the GA results. The results allow
discussing the difference between single and multi-objective approaches
especially for complex systems with a greater number of dependencies
among the classes.
https://hal.inria.fr/hal-01055245 Contributor : Hal IfipConnect in order to contact the contributor Submitted on : Tuesday, August 12, 2014 - 9:20:08 AM Last modification on : Wednesday, August 16, 2017 - 3:22:37 PM Long-term archiving on: : Wednesday, November 26, 2014 - 10:36:21 PM
Rafael Veiga Cabral, Aurora Pozo, Silvia Regina Vergilio. A Pareto Ant Colony Algorithm Applied to the
Class Integration and Test Order Problem. 22nd IFIP WG 6.1 International Conference on Testing Software and Systems (ICTSS), Nov 2010, Natal, Brazil. pp.16-29, ⟨10.1007/978-3-642-16573-3_3⟩. ⟨hal-01055245⟩