Test Data Generation for Multiple Paths Coverage Based on Ant Colony Algorithm

doi:10.3969/j.issn.0372-2112.2020.07.011

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Test Data Generation for Multiple Paths Coverage Based on Ant Colony Algorithm

更新时间：2025-07-08

- Test Data Generation for Multiple Paths Coverage Based on Ant Colony Algorithm
- Acta Electronica Sinica Vol. 48, Issue 7, Pages: 1330-1342(2020)
- 作者机构：
  
  嘉兴学院数理与信息工程学院,浙江,嘉兴,314001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61703183, No.61773410);Public Welfare Technology Application Research of Zhejiang Province (No.LGG19F030010, No.LGG20F010010);Jiaxing Public Welfare Research Project (No.2018AY11008)
- DOI：10.3969/j.issn.0372-2112.2020.07.011
  CLC： TP301
- Published Online：25 July 2020，
  
  Published：2020
- 稿件说明：
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Test Data Generation for Multiple Paths Coverage Based on Ant Colony Algorithm[J]. Acta Electronica Sinica, 2020, 48(7): 1330-1342.
DOI：

Test Data Generation for Multiple Paths Coverage Based on Ant Colony Algorithm[J]. Acta Electronica Sinica, 2020, 48(7): 1330-1342. DOI： 10.3969/j.issn.0372-2112.2020.07.011.

摘要

为了提高多路径覆盖测试数据的生成效率，研究了一种基于蚁群算法的多路径覆盖测试数据生成方法.首先给出蚁群算法的一种改进方法，该算法以蚂蚁对生成测试数据的重要性作为蚂蚁状态转移和蚂蚁路径变异的依据，以引导更多蚂蚁穿越小概率节点，提高测试数据生成效率.其次，根据改进的蚁群算法分别提出了基于单信息素表和多信息素表的多路径覆盖测试数据生成方法.在基于多信息素表的方法中，每条目标路径的信息素表均被用于其它路径测试数据的求解，而且蚁群算法运行一次即可求解多条目标路径的覆盖测试数据.最后对所提出方法的有效性和复杂度进行了理论分析.实验结果表明，与其它方法相比，基于多信息素表的测试数据生成方法能够有效地生成多路径覆盖测试数据.

Abstract

In order to improve the generation efficiency of multipath coverage test data

a novel method is proposed based on ant colony algorithm (ACO). Firstly

an improved ACO is developed. The importance of an ant to generate test data is considered as a factor for ant state transfer and path mutation. As a result

more ants are guided to traverse small probabilities node and the efficiency of test data generation is improved. Secondly

according to the improved ACO

test data generation of multipath coverage based on single pheromone table and multiple pheromone tables are proposed. In a multiple pheromones table based approach

the pheromone table of each target path is also used to generate test data for other target path

and the test data of multiple paths are generated by running ACO only once. Finally

the effectiveness and complexity of the proposed method are analyzed theoretically. Experimental results show that test data generation based on multi-pheromone tables can effectively generate multipath coverage test data compared with other methods.

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