GazePrinter: Visualizing Expert Gaze to Guide Novices in a New Codebase
HCI Today summarized the key points
- •This article discusses the GazePrinter study, which visualizes expert gaze data to help novices understand a new codebase.
- •The researchers note that understanding programs in complex codebases is especially difficult for novices, and propose that guidance using expert gaze is needed.
- •To this end, they build the GazePrinter IntelliJ plugin and conduct a mixed-methods study with 40 novice participants, including surveys, controlled experiments, and interviews.
- •The results suggest that expert gaze visualization can shift novices’ code exploration paths closer to experts’ and may reduce both time and cognitive load.
- •The study shows that gaze-based guidance could be a promising tool for supporting code-reading learning and onboarding, while also calling for further validation.
This summary was generated by an AI editor based on HCI expert perspectives.
Why Read This from an HCI Perspective
From an HCI perspective, this article is worth reading because it shows how ‘gaze-based guidance’ can evolve from a mere tracking technique into a learning-support interface. In particular, it examines how visualizing experts’ exploration paths during onboarding to a new codebase affects novices’ reading strategies, cognitive load, and time efficiency. This makes it especially meaningful for researchers who are thinking about both developer-tool UX and learning-support design.
CIT's Commentary
From a CIT perspective, GazePrinter is an attempt to convey ‘expert knowledge’ not through explanations or comments, but at a more fine-grained level: attentional allocation. What’s interesting is that it can function both as an assistive mechanism for understanding code and as a stepping stone that helps novices internalize experts’ exploration rhythms. However, in real-world settings, expert gaze is not always the correct path, and depending on project context and task goals, it may even risk reinforcing or hardening bias. Therefore, this approach is likely to have stronger HCI value when designed as a ‘scaffolding for exploration’ rather than as ‘presenting the answer.’ CIT interprets key design variables for follow-up work as the visualization intensity, adaptive exposure, and how well users’ self-directed exploration is preserved.
Questions to Consider While Reading
- Q.When expert gaze visualization improves novices’ reading strategies, are there cases where it could instead reduce exploration diversity or reinforce incorrect paths?
- Q.How does GazePrinter’s effectiveness vary with task difficulty or the structural complexity of the codebase, and what would be a way to personalize it?
- Q.When deploying gaze-data-based guidance in a real IDE, how can we define the boundary between visualizations that reduce cognitive load and those that steal attention?
This commentary was generated by an AI editor based on HCI expert perspectives.
Please refer to the original for accurate details.
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