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Implementation of artificial bee colony algorithm for T-way testing

机译:人工蜂群算法在T道测试中的实现

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Literature evidences have demonstrated the effectiveness of the sampled t-way test suite for defect detection in software testing. The main task in implementing t-way testing strategies is constructing best possibility test case. There are several methods that have been proposed but none of them can be claimed to be the best result because t-way are considered as NP-hard problem. In this paper, the concept of artificial bee colony (ABC) will be proposed as the idea in constructing t-way testing test suit. Due to the limitation of classical optimization in solving larger scale combinatorial and highly non-linear problems, researchers are moving to employ the intelligent behaviour of swarm known as Swarm Intelligence. ABC was introduced by Karaboga in 2005 is an optimization algorithm based on honey bee swarm which has been applied in solving real world application.
机译:文献证据证明了采样的t-way测试套件对于软件测试中的缺陷检测的有效性。实施t-way测试策略的主要任务是构建最佳可能性测试用例。已经提出了几种方法,但是由于t-way被认为是NP-hard问题,因此没有一种方法可以说是最好的结果。本文将以人工蜂群(ABC)的概念作为构建t-way测试测试服的构想。由于经典优化方法在解决大规模组合问题和高度非线性问题方面的局限性,研究人员正着手采用称为“群体智能”的群体智能行为。 ABC由Karaboga于2005年推出,是一种基于蜜蜂群的优化算法,已应用于解决现实应用中。

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