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首页> 外文期刊>Journal of Software Engineering Research and Development >An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem
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An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem

机译：一种应用于集成和测试订单问题的多目标算法的一种方法

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摘要

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied to solve hard real software engineering problems. However, to choose and design a MOEA is considered a difficult task, since there are several parameters and components to be configured. These aspects directly impact the generated solutions and the performance of MOEAs. In this sense, this paper proposes an approach for the automatic generation of MOEAs applied to the Integration and Test Order (ITO) problem. Such a problem refers to the generation of optimal sequences of units for integration testing. The approach includes a set of parameters and components of different MOEAs, and is implemented with two design algorithms: Grammatical Evolution (GE) and Iterated Racing (irace). Evaluation results are presented, comparing the MOEAs generated by both design algorithms. Furthermore, the generated MOEAs are compared to two well-known MOEAs used in the literature to solve the ITO problem. Results show that the MOEAs generated with GE and irace perform similarly, and both outperform traditional MOEAs. The approach can reduce efforts spent to design and configure MOEAs, and serves as basis for implementing solutions to other software engineering problems.
机译：多目标进化算法（MOEAS）已成功应用于解决硬真实软件工程问题。但是，要选择和设计，MOEA被认为是一项艰巨的任务，因为有几个参数和要配置的组件。这些方面直接影响了生成的解决方案和MoeS的性能。从这个意义上讲，本文提出了一种自动生成MoES的方法，其应用于集成和测试订单（ITO）问题。这种问题是指用于集成测试的最佳序列。该方法包括一组不同MOES的参数和组件，并用两个设计算法实现：语法演进（GE）和迭代赛车（IRACE）。提出了评估结果，比较了由设计算法产生的MOEAS。此外，将所生成的ModeAs与在文献中使用的两个众所周知的MoeS进行比较以解决ITO问题。结果表明，用GE和IRACE产生的MOEAS同样表现出，并且均优于传统的MOEAS。该方法可以减少花费设计和配置ModEAS的努力，并作为实现其他软件工程问题的解决方案的基础。

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