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Evaluating different strategies for integration testing of aspect-oriented programs

机译:评估面向方面的程序的集成测试的不同策略

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Background The determination of an order for integration and testing of aspects and classes is a difficult optimization problem. This order should be associated to a minimal possible stubbing cost. To determine such order, different approaches exist. For example, traditional approaches are based on Tarjan’s algorithm; search-based approaches are based on metaheuristics, usually genetic algorithms (GA). In addition to such approaches, in the literature, there are different strategies to integrate aspect-oriented software. Some works suggest the integration of aspects and classes in a combined way. Other ones adopt an incremental strategy. Studies evaluating the approaches show that the multi-objective one presents better solutions. However, these studies were conducted applying only the combined strategy. Methods In this paper, we present experimental results comparing both strategies with three different approaches: the traditional one, a simple GA-based, and a multi-objective one. Results The results show better performance of the multi-objective approach independently of the strategy adopted. A comparison of both strategies points out that the incremental strategy reaches a lower cost in most cases, considering a number of attributes and operations to be emulated in the stub. Conclusion It seems that with Incremental+, the best choice is the multi-objective approach. If the system is very complex, PAES seems to be the best MOEA.
机译:背景技术确定方面和类的集成和测试顺序是一个困难的优化问题。该订单应与最小的存根成本相关联。为了确定这种顺序,存在不同的方法。例如,传统方法基于Tarjan的算法;基于搜索的方法基于元启发法,通常是遗传算法(GA)。除了这些方法外,在文献中,还有不同的策略来集成面向方面的软件。一些作品建议以结合的方式整合方面和类。其他的则采用增量策略。评估方法的研究表明,多目标方法提供了更好的解决方案。但是,这些研究仅采用组合策略进行。方法在本文中,我们提供了将三种策略与三种不同方法进行比较的实验结果:传统方法,基于GA的简单方法和多目标方法。结果结果表明,与采用的策略无关,多目标方法的性能更好。两种策略的比较表明,在大多数情况下,考虑到存根中要模拟的许多属性和操作,增量策略的成本较低。结论似乎对于Incremental +,最好的选择是多目标方法。如果系统非常复杂,那么PAES似乎是最好的MOEA。

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