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Application of genetic algorithm for flight system verification and validation

机译:遗传算法在飞行系统验证中的应用

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摘要

Most complex systems nowadays heavily rely on software, and spacecraft and satellite systems are no exception. Moreover as systems capabilities increase, the corresponding software required to integrate and address system tasks becomes more complex. Hence, in order to guarantee a system's success, testing of the software becomes imperative. Traditionally exhaustive testing of all possible behaviors was conducted. However, given the increased complexity and number of interacting behaviors of current systems, the time required for such thorough testing is prohibitive. As a result many have adopted random testing techniques to achieve sufficient coverage of the test space within a reasonable amount of time. In this paper we propose the use of genetic algorithms (GA) to greatly reduce the number of tests performed, while still maintaining the same level of confidence as current random testing approaches. We present a GA specifically tailored for the systems testing domain. In order to validate our algorithm we used the results from the Dawn test campaign. Preliminary results seem very encouraging, showing that our approach, when searching the worst test cases, outperforms random search , limiting the search to a mere 6 % of the full search domain.
机译:如今,大多数复杂系统严重依赖软件,航天器和卫星系统也不例外。此外,随着系统功能的增强,集成和解决系统任务所需的相应软件也变得更加复杂。因此,为了保证系统的成功,必须对软件进行测试。传统上,对所有可能的行为进行了详尽的测试。但是,鉴于当前系统的复杂性和交互行为的数量增加,进行此类彻底测试所需的时间令人望而却步。结果,许多人采用了随机测试技术,以在合理的时间内获得足够的测试空间覆盖率。在本文中,我们建议使用遗传算法(GA)来大大减少执行的测试数量,同时仍保持与当前随机测试方法相同的置信度。我们提供了专门为系统测试领域量身定制的GA。为了验证我们的算法,我们使用了Dawn测试活动的结果。初步结果似乎令人鼓舞,这表明我们的方法在搜索最差的测试用例时胜过随机搜索,将搜索范围限制在整个搜索域的6%。

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