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Multifaceted Test Suite Generation Using Primary and Supporting Fitness Functions

机译：使用主要和支持的适应度函数生成多方面的测试套件

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摘要

Dozens of criteria have been proposed to judge testing adequacy. Such criteria are important, as they guide automated generation ef- forts. Yet, the current use of such criteria in automated generation contrasts how such criteria are used by humans. For a human, coverage is part of a multifaceted combination of testing strategies. In automated generation, coverage is typically the goal, and a single fitness function is applied at one time. We propose that the key to improving the fault detection efficacy of search-based test generation approaches lies in a targeted, multifaceted approach pairing primary fitness functions that effectively explore the structure of the class under test with lightweight supporting fitness functions that target particular scenarios likely to trigger an observable failure. This report summarizes our findings to date, details the hypothesis of primary and supporting fitness functions, and identifies outstand- ing research challenges related to multifaceted test suite generation. We hope to inspire new advances in search-based test generation that could benefit our software-powered society.
机译：已经提出了数十种标准来判断测试的充分性。这些标准很重要，因为它们指导了自动发电的努力。但是，当前在自动生成中使用这些标准的方式与人类如何使用这些标准形成了对比。对于人类而言，覆盖范围是测试策略多方面组合的一部分。在自动生成中，覆盖通常是目标，并且一次应用单个适应性功能。我们提出，提高基于搜索的测试生成方法的故障检测效率的关键在于有针对性的，多方面的方法将主要适应度函数与有效适应性强的适应性函数的轻量级支持适应性函数对被测类的结构进行匹配，从而针对特定情况触发可观察到的故障。本报告总结了迄今为止我们的发现，详细介绍了主要功能和支持功能的假设，并确定了与多方面测试套件生成相关的突出研究挑战。我们希望激发基于搜索的测试生成方面的新进展，这可以使我们的软件驱动的社会受益。

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