t-Way测试套件生成的人工蜂群算法

A. K. Alazzawi, Helmi Md Rais, S. Basri
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引用次数: 12

摘要

详尽测试是一项非常艰苦的工作,因为它有许多组合。因此,从可行的测试用例组(TC)中搜索并抽样一个最优的测试套件已被证明是一个中心问题。为了解决这个问题,使用t-way测试(t表示相互作用的强度)已经广为人知。为了便于未来的发展和总结迄今为止的实现,本文的主要目的是对所引入的优化算法(OA)进行重要的比较,作为t-way测试集生成策略的基础,并提出一种以人工蜂群(ABC)算法为重点的新策略,称为人工蜂群策略(ABCS)。实验结果表明,ABCS在生成最优测试用例方面可以与现有的(基于人工智能和基于计算的)策略竞争。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Bee Colony Algorithm for t-Way Test Suite Generation
Exhaustive testing is a very strenuous undertaking due to its numerous combinations. Consequently, searching for and sampling an optimal test suite from viable groups of test cases (TC) has turned out to be a central concern. To tackle this matter, the use of t-way testing (Where t represents the strength of the interaction) has become well known. In order to facilitate future development and summarize the realization so far, the major objective of this paper is to portray an important comparison of the introduced optimization algorithms (OA) as a base of the t-way test suite generation strategy, and to suggest a new strategy focusing on the Artificial Bee Colony (ABC) Algorithm, known as Artificial Bee Colony Strategy (ABCS). The experimental results showed that ABCS can compete against the existing (both AI-based and computational-based) strategies in terms of generating the optimum test case.
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