遗传算法在软件测试中的局限性

S. Aljahdali, Ahmed S. Ghiduk, M. El-Telbany
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引用次数: 20

摘要

软件测试数据生成是识别满足给定测试标准的一组数据的过程。为了解决这一难题,过去人们做了大量的研究工作。最常见的是随机测试数据生成、符号测试数据生成、动态测试数据生成,以及最近基于遗传算法的测试数据生成。本文综述了大多数基于遗传算法的软件测试数据生成技术。根据遗传算法的特点和参数对所调查的技术进行了比较和分类。此外,本文还对这些技术的局限性进行了说明和分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The limitations of genetic algorithms in software testing
Software test-data generation is the process of identifying a set of data, which satisfies a given testing criterion. For solving this difficult problem there were a lot of research works, which have been done in the past. The most commonly encountered are random test-data generation, symbolic test-data generation, dynamic test-data generation, and recently, test-data generation based on genetic algorithms. This paper gives a survey of the majority of software test-data generation techniques based on genetic algorithms. It compares and classifies the surveyed techniques according to the genetic algorithms features and parameters. Also, this paper shows and classifies the limitations of these techniques.
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