gazel: Supporting Source Code Edits in Eye-Tracking Studies

TL;DR

Gazel introduces a novel system that enhances eye-tracking studies in software engineering by accurately mapping gaze data to source code during editing activities, enabling more realistic and comprehensive research.

Contribution

The paper presents iTrace-Atom and gazel, new tools that support eye tracking during source code edits, overcoming previous limitations and improving accuracy in dynamic coding environments.

Findings

iTrace-Atom achieves over 99% accuracy at high eye-tracking speeds

Gazel enables tracking gaze during code editing, scrolling, and context switching

The system facilitates realistic software engineering studies involving editing tasks

Abstract

Eye tracking tools are used in software engineering research to study various software development activities. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace community infrastructure. We introduce the iTrace-Atom plugin and gazel -- a Python data processing pipeline that maps gaze information to changing source code elements and provides researchers with a way to query this dynamic data. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. iTrace and gazel completely revolutionize the way eye tracking studies are conducted in realistic settings with the presence of scrolling, context switching, and now editing. This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring.