Optimization of Web Service Testing Task Assignment in Crowdtesting Environment

Optimization of Web Service Testing Task Assignment in Crowdtesting Environment
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众测环境下Web服务测试任务分配的优化

DOI:
10.1007/s11390-022-0824-7
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发表时间:
2023-03
影响因子:
0.7
通讯作者:
Shikai Guo
Shikai Guo
中科院分区:
--
文献类型:
--
作者:
Wenjun Tang;Rong Chen;Jiali Zhang;Lin Huang;Shengjie Zheng;Shikai Guo

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众测已经成为一种极具吸引力且经济的测试模式,它的特点是测试者来自不同的国家,有着不同的背景和工作条件。最近在众包测试方面的发展表明,管理测试群体和过程是可行的,但是它们通常超出了标准测试理论的范围。本文探讨了在不断变化的众包环境中,如何将服务测试任务分配给合适的测试人员。我们将其形式化为一个优化问题,以保证人群的测试质量为目标,同时考虑知识能力、奖励、网络连接、地理位置和所需技能等影响因素。为了解决上述问题,我们设计了一种基于差分进化(DE)算法的任务分配算法。在实际数据和综合数据中进行了大量的实验,以评估该算法的效率和有效性,结果表明,与其他启发式算法相比,该算法具有更好的性能。
Crowdtesting has emerged as an attractive and economical testing paradigm that features testers from different countries, with various backgrounds and working conditions. Recent developments in crowdsourcing testing suggest that it is feasible to manage test populations and processes, but they are often outside the scope of standard testing theory. This paper explores how to allocate service-testing tasks to proper testers in an ever-changing crowdsourcing environment. We formalize it as an optimization problem with the objective to ensure the testing quality of the crowds, while considering influencing factors such as knowledge capability, the rewards, the network connections, and the geography and the skills required. To solve the proposed problem, we design a task assignment algorithm based on the Differential Evolution (DE) algorithm. Extensive experiments are conducted to evaluate the efficiency and effectiveness of the proposed algorithm in real and synthetic data, and the results show better performance compared with other heuristic-based algorithms.
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