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For instance, the classes and aspects can be integrated in a combined strategy, or in an incremental way. The few works evaluating such strategies do not consider the multi-objective and coupling based approach. Given the importance of such approach to reduce testing efforts, in this work, we conduct an empirical study and present results from the application of the multi-objective approach with both mentioned strategies. The approach is implemented with four coupling measures and three evolutionary algorithms that are also evaluated: NSGA-II, SPEA2 and PAES.

It is expected that the Incremental strategy influences the exploitation on the search space due to its rule that integrates classes first and aspects later. In our study context, both strategies achieve good results in order to make the integration test of aspect-oriented software less labour-intensive and expensive. The kind of evolutionary algorithm seems not to influence on the strategy performance, but we observed that the Incremental strategy explores the search space in a different way and finds the best results with PAES for one system.

Assun¸c˜ ao et al. The measures A and O are commonly used in related work. In the other hand, the measures R and P allow to consider different factors related directly to the stub complexity. Furthermore, O, R and P are interdependent. 3 Multi-Objective Optimization This step is the application of the multi-objective evolutionary algorithm (MOEA). In our study we used three variants of GAs, which adopt different evolution and diversification methodologies. They are: NSGA-II (Non-dominated Sorting Genetic Algorithm) [9], SPEA2 (Strength Pareto Evolutionary Algorithm) [27] and PAES (Pareto Archived Evolution Strategy) [14].

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Agilent - 89600 Series VSA Software for IEEE 802.16 OFDMA Evaluation and Troubleshooting

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