Robust web element identification for evolving applications by considering visual overlaps

Abstract

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 web applications 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.

Similarity-based Web Element Localization for Robust Test Automation

“…See definitions in Section VI. The three categories were coded based on literature that utilizes abilities in traditional algorithms, NLP, or LLMs when comparing GUI elements or creating input for testing [18], [19], [23], [24], [25].…”

“…Some of the more recent approaches use similarity scores to identify the most similar web element to a target. This is done by using previously stored properties (i.e., extracted from the corresponding web element in a previous version of the web application) and comparing the stored properties to the updated web elements [18], [19]. The web element with the highest score is assumed to be the most likely web element to use in an interaction (e.g., a click or type action).…”