Erratum: Leveraging Flexible Tree Matching to Repair Broken Locators in Web Automation Scripts
Not on the map. Why?
Abstract (arXiv)
Web applications are constantly evolving to integrate new features and fix reported bugs. Even an imperceptible change can sometimes entail significant modifications of the Document Object Model (DOM), which is the underlying model used by browsers to render all the elements included in a web application. Scripts that interact with web applications (e.g. web test scripts, crawlers, or robotic process automation) rely on this continuously evolving DOM which means they are often particularly fragile. More precisely, the major cause of breakages observed in automation scripts are element locators, which are identifiers used by automation scripts to navigate across the DOM. When the DOM evolves, these identifiers tend to break, thus causing the related scripts to no longer locate the intended target elements. For this reason, several contributions explored the idea of automatically repairing broken locators on a page. These works attempt to repair a given broken locator by scanning all elements in the new DOM to find the most similar one. Unfortunately, this approach fails to scale when the complexity of web pages grows, leading either to long computation times or incorrect element repairs. This article, therefore, adopts a different perspective on this problem by introducing a new locator repair solution that leverages tree matching algorithms to relocate broken locators. This solution, named Erratum, implements a holistic approach to reduce the element search space, which greatly eases the locator repair task and drastically improves repair accuracy. We compare the robustness of Erratum on a large-scale benchmark composed of realistic and synthetic mutations applied to popular web applications currently deployed in production. Our empirical results demonstrate that Erratum outperforms the accuracy of WATER, a state-of-the-art solution, by 67%.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jun 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2026Plover: Steering GUI Agents through Plan-Centric Interaction16 Jul 2026
- 2026WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing26 Mar 2026
- 2026SPRITE: From Static Mockups to Engine-Ready Game UI18 Mar 2026
- 2025Are Autonomous Web Agents Good Testers?2 Apr 2025