Assessing Python Style Guides: An Eye-Tracking Study with Novice Developers
Pablo Roberto, Rohit Gheyi, Jos\'e Aldo Silva da Costa, M\'arcio, Ribeiro

TL;DR
This eye-tracking study with novice Python developers evaluates how adherence to PEP8 style guide recommendations affects code readability and visual effort, highlighting specific guidelines that improve or hinder understanding.
Contribution
It provides empirical evidence on the impact of PEP8 style guidelines on novice developers' reading effort using eye-tracking metrics.
Findings
Not following the Line Break after an Operator increases eye regressions by 70%.
Most subjects preferred PEP8-compliant code despite some guidelines negatively affecting eye metrics.
Guidelines like True Comparison negatively impacted eye metrics, yet were preferred by subjects.
Abstract
The incorporation and adaptation of style guides play an essential role in software development, influencing code formatting, naming conventions, and structure to enhance readability and simplify maintenance. However, many of these guides often lack empirical studies to validate their recommendations. Previous studies have examined the impact of code styles on developer performance, concluding that some styles have a negative impact on code readability. However, there is a need for more studies that assess other perspectives and the combination of these perspectives on a common basis through experiments. This study aimed to investigate, through eye-tracking, the impact of guidelines in style guides, with a special focus on the PEP8 guide in Python, recognized for its best practices. We conducted a controlled experiment with 32 Python novices, measuring time, the number of attempts, and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWikis in Education and Collaboration · Educational Games and Gamification · Data Visualization and Analytics
