可视化测试多样性以支持测试优化
Visualizing test diversity to support test optimisation
摘要：
多样性已被用作一个有效的标准，以优化测试套件的成本效益的测试。特别地，基于多样性（或者称为基于相似性）的技术具有通用性和适用于不同的被测系统（SUT）的优点，并且已经用于自动选择或优先化大的测试用例集。然而，将多样性信息反馈给开发人员和测试人员是一个挑战，因为结果通常是多维的。此外，基于多样性的方法具有普遍性，因此更难选择何时何地适用这些方法。在本文中，我们通过调查来解决这些挑战：i）在使用不同的多样性来源（例如，测试需求或测试脚本的多样性），以优化大型测试套件，以及ii）测试多样性数据的可视化如何帮助测试人员进行测试优化和改进。我们对三个工业项目进行了案例研究，并给出了不同测试用例集的故障检测能力和冗余水平的定量结果。我们的主要结果是，基于成对多样性计算的测试相似性图，帮助行业从业者识别测试存储库的问题，并决定改进措施。我们的结论是多样性信息的可视化可以帮助测试人员在他们的维护和优化活动。
英文摘要：
Diversity has been used as an effective criteria to optimise test suites for cost-effective testing. Particularly, diversity-based (alternatively referred to as similarity-based) techniques have the benefit of being generic and applicable across different Systems Under Test (SUT), and have been used to automatically select or prioritise large sets of test cases. However, it is a challenge to feedback diversity information to developers and testers since results are typically many-dimensional. Furthermore, the generality of diversity-based approaches makes it harder to choose when and where to apply them. In this paper we address these challenges by investigating: i) what are the trade-off in using different sources of diversity (e.g., diversity of test requirements or test scripts) to optimise large test suites, and ii) how visualisation of test diversity data can assist testers for test optimisation and improvement. We perform a case study on three industrial projects and present quantitative results on the fault detection capabilities and redundancy levels of different sets of test cases. Our key result is that test similarity maps, based on pair-wise diversity calculations, helped industrial practitioners identify issues with their test repositories and decide on actions to improve. We conclude that the visualisation of diversity information can assist testers in their maintenance and optimisation activities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
