论文本测试的工业适用性:一个实证案例研究

Emil Alégroth, Geoff Bache, E. Bache
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引用次数: 2

摘要

软件系统正变得越来越复杂,尤其是它们的图形用户界面(gui),这对现有的测试实践提出了挑战。减少上市时间的压力减少了手工测试的时间,并增加了测试自动化的重要性。以前的研究已经确定了几代基于gui的自动化测试方法,它们具有不同的成本效益权衡。虽然测试自动化提供了快速的质量反馈,但它可能与高成本和无法识别测试设计者没有明确预期的缺陷联系在一起。TextTest是一种用于基于gui的测试的捕获-重放工具,它采用了一种新颖的方法,克服了以前使用方法遇到的一些挑战。首先,该工具支持批准测试,这是一种使用GUI的可视化状态的ascii艺术表示来在系统级别验证正确的应用程序行为的方法。其次,它用用户定义的领域特定语言(DSL)记录和重放测试脚本,所有涉众都可以阅读。在本文中,我们提出了一个分三个阶段的工业案例研究,旨在确定textttest在工业实践中的适用性。该论文报告了该工具与(1)由于记录功能的低脚本开发成本,(2)低维护成本,平均每个测试用例7分钟,(3)比手工系统测试更好的缺陷发现能力,(4)高测试用例执行性能(在这种情况下,20分钟内500个测试用例),(5)由于DSL定义的脚本的高脚本可读性,以及(6)健壮的测试套件变更(在这种情况下,每次迭代93%)。然而,该工具需要更高程度的技术技能来进行定制工作,测试维护者需要设计正则表达式的技能,并且该工具的适用性目前仅限于基于Java和Python的应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Industrial Applicability of TextTest: An Empirical Case Study
Software systems are becoming more complex, not least in their Graphical User Interfaces (GUIs), which presents challenges for existing testing practices. Pressure to reduce time to market leaves less time for manual testing and increases the importance of test automation. Previous research has identified several generations of automated GUI-based test approaches with different cost-benefit tradeoffs. Whilst test automation provides fast quality feedback it can be associated with high costs and inability to identify defects not explicitly anticipated by the test designer. TextTest is a capture-replay tool for GUI-based testing with a novel approach that overcomes several of the challenges experienced with previous approaches. Firstly the tool supports Approval Testing, an approach where ASCII-art representations of the GUI's visual state are used to verify correct application behavior at the system level. Secondly it records and replays test scripts in a user defined domain specific language (DSL) that is readable by all stakeholders. In this paper we present a three phase industrial case study that aims to identify TextTest's applicability in industrial practice. The paper reports that the tool is associated with (1) low script development costs due to recording functionality, (2) low maintenance costs, on average 7 minutes per test case, (3) better defect finding ability than manual system testing, (4) high test case execution performance (In this case 500 test cases in 20 minutes), (5) high script readability due to DSL defined scripts, and (6) test suites that are robust to change (In this case 93 percent per iteration). However, the tool requires a higher degree of technical skill for customization work, test maintainers need skills in designing regular expressions and the tool's applicability is currently restricted to Java and Python based applications.
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