Enhanced Genetic Algorithm for MC/DC test data generation

A. El-Serafy, G. El-Sayed, Cherif R. Salama, A. Wahba
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引用次数: 12

Abstract

Structural testing is concerned with the internal structures of the written software. The targeted structural coverage criteria are usually based on the criticality of the application. Modified Condition/Decision Coverage (MC/DC) is a structural coverage criterion that was introduced to the industry by NASA. Also, MC/DC comes either highly recommended or mandated by multiple standards, including ISO 26262 from the automotive industry and DO-178C from the aviation industry due to its efficiency in bug finding while maintaining a compact test suite. However, due to its complexity, huge amount of resources are dedicated to fulfilling it. Hence, automation efforts were directed to generate test data that satisfy MC/DC. Genetic Algorithms (GA) in particular showed promising results in achieving high coverage percentages. Our results show that coverage levels could be further improved using a batch of enhancements applied on the GA search.
MC/DC测试数据生成的改进遗传算法
结构测试关注的是编写软件的内部结构。目标结构覆盖标准通常基于应用程序的临界性。修正条件/决策覆盖(MC/DC)是由NASA引入的一种结构覆盖标准。此外,MC/DC受到多个标准的强烈推荐或强制要求,包括来自汽车行业的ISO 26262和来自航空行业的DO-178C,因为它在保持紧凑测试套件的同时有效地发现错误。然而,由于其复杂性,需要投入大量的资源来实现它。因此,自动化工作被导向生成满足MC/DC的测试数据。遗传算法(GA)在实现高覆盖率方面尤其显示出令人鼓舞的结果。我们的结果表明,通过对GA搜索进行一批增强,可以进一步提高覆盖水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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