Building Ant System for Multi-Faceted Test Case Prioritization

M. K. Pachariya
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引用次数: 1

Abstract

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.
构建面向多面测试用例优先排序的蚂蚁系统
本文提出了多标准测试用例优先级的实证研究。在本文中,使用蚁群优化(ACO)方法解决了具有时间约束的测试用例优先级问题。ACO是一种元启发式和自然启发的方法,已应用于基于覆盖率的测试用例优先级问题的陈述。所建议的方法使用语句覆盖率作为适应度标准,并将执行时间作为约束对测试用例进行排序。该方法在MatLab中实现,并在广泛使用的基准数据集上进行了验证,该数据集可在软件基础架构存储库(SIR)上免费获得。实验研究结果表明,基于蚁群算法的测试用例优先级问题提供了近似最优解。
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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