PosEyeDOM: Lightweight Positional Copilot Suggestion Logger for Eye-Tracking Applications using the DOM

Understanding the impact of GenAI code suggestions on developer productivity requires studying how developers actually interact with them. Yet, inline suggestions from assistants such as GitHub Copilot appear and disappear quickly, shift with scrolling, and range from single-line completions to multi-line overlays. Capturing interactions is especially difficult for eye-tracking, which depends on precise positional data and timing. Existing tools offer limited support, as these transient overlays may disappear without ever being integrated into the source code. PosEyeDOM addresses this gap with a lightweight Chrome extension for web-based VS Code environments such as GitHub Codespaces. By leveraging the Document Object Model (DOM), it continuously logs suggestion geometry, content, and timing in real-time, infers acceptance outcomes, and aggregates them into robust and labeled Areas of Interest (AOIs). It exports ready-to-use .ias files compatible with SR Research DataViewer and JSON dictionaries for custom pipelines, enabling scalable, suggestion-aware eye-tracking studies that were previously impractical. The extension further provides a live preview, remote export, and synchronization with external eye-tracking software, offering an all-in-one, plug-and-play workflow for empirical research on AI-assisted programming.