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Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software

机译：自适应进化测试：面向对象软件的基于搜索的测试用例生成的自适应方法

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摘要

Adaptive Evolutionary Algorithms are distinguished by their dynamic manipulation of selected parameters during the course of evolving a problem solution; they have an advantage over their static counterparts in that they are more reactive to the unanticipated particulars of the problem. This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an Adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the test case generation algorithm's efficiency considerably, while introducing a negligible computational overhead.
机译：自适应进化算法的特征在于，它们在解决问题的过程中可以动态地选择参数。与静态对象相比，它们具有优势，因为它们对问题的意外细节更具反应性。本文提出了一种自适应策略，用于增强基于遗传编程的自动测试用例生成方法。这项研究的主要贡献是提出了一种自适应进化测试方法，以促进通过突变将相关指令引入生成的测试案例中。对可以从中选择算法的指令进行排名，并根据从上一代评估的个体获得的反馈，每代更新其排名。开展的实验研究表明，所提出的自适应策略大大提高了测试案例生成算法的效率，同时引入了可忽略的计算开销。

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