基于模型的测试序列生成与蚁群优化

Gayatri Nayak, M. Ray
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引用次数: 0

摘要

本文提出了一种从输入UML活动图生成和优化测试序列的方法。为此,提出了一种名为“测试序列生成统一建模语言”(UMLTSG)的算法,该算法使用一种名为“使用蚁群优化的测试序列优先化”(TSP ACO)的基于搜索的算法来生成和优化测试序列。该算法克服了处理复杂决策活动(如条件活动、分叉活动和加入活动)的现有限制。优化过程有助于减少处理节点的数量,从而最大限度地减少时间和成本。提出的方法在一个著名的应用程序“铁路订票系统”(RTRS)上进行了实验。APFD度量度量了我们的方法的有效性,并发现测试序列的优先顺序达到了高出20%的APFD分数。除此之外,我们还在六个实际案例研究中进行了实验,并获得了冗余测试路径平均减少52.16%的结果。
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Model-Based Test Sequence Generation and Prioritization Using Ant Colony Optimization
The paper presents an approach to generate and optimize test sequences from the input UML activity diagram. For this, an algorithm is proposed called “Unified Modelling Language for Test Sequence Generation" (UMLTSG) that uses a search-based algorithm, named “Test Sequence Prioritization using Ant Colony Optimization" (TSP ACO) to generate and optimize test sequences. The algorithms overcome the existing limitations of handling complex decision-making activity such as conditional activity, fork activity, and join the activity. The optimization process helps to reduce the number of processing nodes that leads to minimizing the time and cost. The proposed approach experiments on a well-known application “Railway Ticket Reservation System" (RTRS). APFD metric measures the effectiveness of our approach and found that the prioritized order of test sequences achieved 20% higher APFD score. Apart from this, we have also experimented on six real life case studies and obtained an average of 52.16% reduction in redundant test paths.
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