Fragile (i.e., non-robust) test execution is a common challenge for automated GUI-based testing of web applications as they evolve. Despite recent progress, there is still room for improvement since test execution failures caused by technical limitations result in unnecessary maintenance costs that limit its effectiveness and efficiency. One of the most reported technical challenges for web-based tests concerns how to reliably locate a web element used by a test script. This paper proposes the novel concept of Visually Overlapping Nodes (VON) that reduces fragility by utilizing the phenomenon that visual web elements (observed by the user) are constructed from multiple web-elements in the Document Object Model (DOM) that overlaps visually. We demonstrate the approach in a tool, VON Similo, which extends the state-of-the-art multi-locator approach (Similo) that is also used as the baseline for an experiment. In the experiment, a ground truth set of 1163 manually collected web element pairs, from different releases of the 40 most popular websites on the internet, are used to compare the approaches' precision, recall, and accuracy. Our results show that VON Similo provides 94.7% accuracy in identifying a web element in a new release of the same SUT. In comparison, Similo provides 83.8% accuracy. These results demonstrate the applicability of the visually overlapping nodes concept/tool for web element localization in evolving web applications and contribute a novel way of thinking about web element localization in future research on GUI-based testing.
翻译:易碎( 即非紫外线) 测试执行是自动图形界面测试网络应用程序演变过程中的常见挑战。 尽管最近有所进步, 但由于技术限制导致的测试执行失败导致不必要的维护成本, 因而仍有改进的余地。 网络测试中报告最多的技术挑战之一是如何可靠定位测试脚本使用的网络元件。 本文提出了视觉重叠节点( VON) 的新概念, 利用以下现象来降低脆弱性: 视觉化的网络元素( 用户所观测的) 是从视觉化的文档对象模型( DOM) 的多个网络元素中构建的, 具有视觉重叠。 我们用一个工具( VON Similo) 展示了测试方法, VON Similo, 扩展了最新的多定位方法( Simillo) 。 在试验中, VON Similo- lax 的网络化版本中, 提供了一个新的网络化定义的精确度。 在网络测试中, Vimilo- 94 的精确度显示Simal- laim- ebal imal ealation ealation laction lab ement lab lab exalation lab lab lab ement labaliz lacalmentalmentalationalation labalationalization labal lab labalizebal lab labal ebalizationalizationalizalizationalizationalization labalizationalizationalizationalization labalizational ex