MC/DC测试数据生成的改进遗传算法

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2015-09-01 DOI:10.1109/INISTA.2015.7276794

A. El-Serafy, G. El-Sayed, Cherif R. Salama, A. Wahba

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引用次数: 12

摘要

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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Enhanced Genetic Algorithm for MC/DC test data generation

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.

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来源期刊

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

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