TESTQUEST: A Web Gamification Tool to Improve Locators and Page Objects Quality

• URL: http://arxiv.org/abs/2505.24756v1
• Date: Fri, 30 May 2025 16:18:10 GMT
• Title: TESTQUEST: A Web Gamification Tool to Improve Locators and Page Objects Quality
• Authors: Dario Olianas, Diego Clerissi, Maurizio Leotta, Filippo Ricca,
• Abstract summary: TestQUEST is a tool designed to improve test robustness by applying to locators and Page Objects.<n> locators are highly sensitive to the frequent changes in Web page structures caused by rapid software evolution.
• Score: 2.156170153103442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Web applications play a crucial role in our daily lives, making it essential to employ testing methods that ensure their quality. Typically, Web testing automation frameworks rely on locators to interact with the graphical user interface, acting as connection points to the elements on a Web page. Nevertheless, locators are widely recognized as a major vulnerability in Web testing, as they are highly sensitive to the frequent changes in Web page structures caused by rapid software evolution. The adoption of the Page Object pattern to separate test logic from structural layout - supporting code reuse and maintainability - has generally led to more robust test cases. However, their implementation is a manually intensive task, and even automated support may require manual realignment efforts. Although gamification strategies have recently been integrated into the Web testing process to boost user engagement, using tasks and rewards aligned with testing activities, they have not yet been employed to enhance the robustness of locators and support the implementation of Page Objects. In this paper, we introduce TESTQUEST, a tool designed to improve test robustness by applying gamification to locators and Page Objects, boosting user engagement while guiding them toward the adoption of best practices.

Related papers

• Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts [59.68272935616536]
  Avenir-Web is a web agent that achieves a new open-source state of the art on the Online-Mind2Web benchmark in real-world deployment.<n>We evaluate Avenir-Web on Online-Mind2Web, a rigorous benchmark of live and user-centered web tasks.
  arXiv  Detail & Related papers  (2026-02-02T18:50:07Z)
• WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents [31.554790282560443]
  We introduce WebArbiter, a principle-inducing WebPRM that formulates reward modeling as text generation.<n>WebArbiter produces structured justifications that conclude with a preference verdict and identify the action most conducive to task completion.
  arXiv  Detail & Related papers  (2026-01-29T15:39:50Z)
• Nested Browser-Use Learning for Agentic Information Seeking [60.775556172513014]
  Information-seeking (IS) agents have achieved strong performance across a range of wide and deep search tasks, yet their tool use remains largely restricted to API-level snippet retrieval and URL-based page fetching.<n>We propose Nested Browser-Use Learning (NestBrowse), which introduces a minimal and complete browser-action framework that decouples interaction control from page exploration through a nested structure.
  arXiv  Detail & Related papers  (2025-12-29T17:59:14Z)
• WALT: Web Agents that Learn Tools [66.73502484310121]
  WALT is a framework that reverse-engineers latent website functionality into reusable invocable tools.<n>Rather than hypothesizing ad-hoc skills, WALT exposes robust implementations of automations already designed into websites.<n>On VisualWebArena and WebArena, WALT achieves higher success with fewer steps and less LLM-dependent reasoning.
  arXiv  Detail & Related papers  (2025-10-01T23:41:47Z)
• AI Agents for Web Testing: A Case Study in the Wild [20.669140680308494]
  We present WebProber, a prototype AI agent-based web testing framework.<n>Given a URL, WebProber autonomously explores the website, simulating real user interactions, identifying bugs and usability issues, and producing a human-readable report.<n>We evaluate WebProber through a case study of 120 academic personal websites, where it uncovered 29 usability issues--many of which were missed by traditional tools.
  arXiv  Detail & Related papers  (2025-09-05T15:57:16Z)
• WebDS: An End-to-End Benchmark for Web-based Data Science [59.270670758607494]
  WebDS is the first end-to-end web-based data science benchmark.<n>It comprises 870 web-based data science tasks across 29 diverse websites.<n>WebDS sets the stage for significant advances in the development of practically useful LLM-based data science.
  arXiv  Detail & Related papers  (2025-08-02T06:39:59Z)
• Reinforcement Learning for Automated Cybersecurity Penetration Testing [0.0]
  Reinforcement Learning is proposed to select and prioritize tools and optimize the testing path.<n>The validation and testing process was conducted on real-world vulnerable web pages commonly used by human hackers for learning.
  arXiv  Detail & Related papers  (2025-06-30T15:06:17Z)
• Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs [0.5965410190046627]
  This paper presents an automated system for generating test cases for two key aspects of web application testing: site navigation and form filling.<n>For site navigation, the system employs screen transition graphs and LLMs to model navigation flows and generate test scenarios.<n>For form filling, it uses state graphs to handle conditional forms and automates Selenium script generation.
  arXiv  Detail & Related papers  (2025-06-03T07:08:21Z)
• WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback [74.82886755416949]
  We identify key reasoning skills essential for effective web agents.<n>We reconstruct the agent's reasoning algorithms into chain-of-thought rationales.<n>Our approach yields significant improvements across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T14:03:37Z)
• Enhancing Web Agents with Explicit Rollback Mechanisms [55.276852838877346]
  We enhance web agents with an explicit rollback mechanism, enabling the agent to revert back to a previous state in its navigation trajectory.<n>This mechanism gives the model the flexibility to directly control the search process, leading to an effective and efficient web navigation method.
  arXiv  Detail & Related papers  (2025-04-16T05:41:20Z)
• An Autonomous RL Agent Methodology for Dynamic Web UI Testing in a BDD Framework [0.0]
  This paper introduces an autonomous reinforcement learning (RL) agent integrated within a Behavior-Driven Development (BDD) framework to enhance user interface testing.<n>By leveraging the adaptive decision-making capabilities of RL, the proposed approach dynamically generates and refines test scenarios aligned with specific business expectations and actual user behavior.
  arXiv  Detail & Related papers  (2025-03-11T14:12:50Z)
• Automated Penetration Testing: Formalization and Realization [0.8684482273743697]
  Recent changes in standards and regulations mandate increased security testing of software systems.<n>Manual penetration testing is labor-intensive and requires highly skilled practitioners.<n>We propose a general self-organizing architecture that can be instantiated to automate penetration testing of real systems.
  arXiv  Detail & Related papers  (2024-12-17T10:09:41Z)
• PAFFA: Premeditated Actions For Fast Agents [19.576180667174366]
  We introduce PAFFA, a method that makes LLMs faster and more accurate in completing tasks on the internet using a novel inference-time technique.<n>PAFFA drastically reduces inference time tokens by 87% while maintaining robust performance.<n>Unravel's ability to update its action library based on explorations allows generalization and adaptation to unseen websites.
  arXiv  Detail & Related papers  (2024-12-10T22:51:31Z)
• The BrowserGym Ecosystem for Web Agent Research [151.90034093362343]
  BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents.<n>We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature.<n>We conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks.
  arXiv  Detail & Related papers  (2024-12-06T23:43:59Z)
• Leveraging Large Vision Language Model For Better Automatic Web GUI Testing [7.480576630392405]
  This paper proposes VETL, the first LVLM-driven endtoend web testing technique. With LVLM's scene understanding capabilities, VETL can generate valid and meaningful text inputs focusing on the local context. The selection of associated GUI elements is formulated as a visual question-answering problem, allowing LVLM to capture the logical connection between the input box and the relevant element.
  arXiv  Detail & Related papers  (2024-10-16T01:37:58Z)
• WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models [65.18602126334716]
  Existing web agents typically only handle one input modality and are evaluated only in simplified web simulators or static web snapshots. We introduce WebVoyager, an innovative Large Multimodal Model (LMM) powered web agent that can complete user instructions end-to-end by interacting with real-world websites. We show that WebVoyager achieves a 59.1% task success rate on our benchmark, significantly surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text-only) setups.
  arXiv  Detail & Related papers  (2024-01-25T03:33:18Z)
• Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
  We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf) For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
  arXiv  Detail & Related papers  (2021-04-16T21:35:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.