Exploring the Capabilities of Vision-Language Models to Detect Visual Bugs in HTML5 <canvas> Applications

TL;DR

This paper investigates the use of Vision-Language Models to detect visual bugs in HTML5 <canvas> applications, including procedurally generated graphics, by providing contextual information to achieve high accuracy.

Contribution

It demonstrates that Vision-Language Models can effectively detect visual bugs in <canvas> applications using contextual information, even with procedural graphics.

Findings

VLMs achieved up to 100% accuracy in bug detection.

Providing application README and bug descriptions improves detection performance.

The approach works across multiple bug types and procedural graphics.

Abstract

The HyperText Markup Language 5 (HTML5) <canvas> is useful for creating visual-centric web applications. However, unlike traditional web applications, HTML5 <canvas> applications render objects onto the <canvas> bitmap without representing them in the Document Object Model (DOM). Mismatches between the expected and actual visual output of the <canvas> bitmap are termed visual bugs. Due to the visual-centric nature of <canvas> applications, visual bugs are important to detect because such bugs can render a <canvas> application useless. As we showed in prior work, Asset-Based graphics can provide the ground truth for a visual test oracle. However, many <canvas> applications procedurally generate their graphics. In this paper, we investigate how to detect visual bugs in <canvas> applications that use Procedural graphics as well. In particular, we explore the potential of Vision-Language Models (VLMs) to automatically detect visual bugs. Instead of defining an exact visual test oracle, information about the application's expected functionality (the context) can be provided with the screenshot as input to the VLM. To evaluate this approach, we constructed a dataset containing 80 bug-injected screenshots across four visual bug types (Layout, Rendering, Appearance, and State) plus 20 bug-free screenshots from 20 <canvas> applications. We ran experiments with a state-of-the-art VLM using several combinations of text and image context to describe each application's expected functionality. Our results show that by providing the application README(s), a description of visual bug types, and a bug-free screenshot as context, VLMs can be leveraged to detect visual bugs with up to 100% per-application accuracy.