An Exploratory Eye Tracking Study on How Developers Classify and Debug Python Code in Different Paradigms
Samuel W. Flint, Jigyasa Chauhan, Niloofar Mansoor, Bonita Sharif, Robert Dyer

TL;DR
This study uses eye-tracking to explore how developers classify and debug Python code across different paradigms, revealing paradigm-specific comprehension challenges and reading patterns.
Contribution
It provides novel empirical insights into how paradigm features influence code comprehension and debugging, highlighting differences in reading behavior and performance.
Findings
Confusion between Functional and Procedural paradigms
Functional code takes longer to debug
Changing paradigms does not affect debugging success
Abstract
Modern programming languages, such as Python, support language features from several paradigms, such as object-oriented, procedural, and functional. Research has shown that code written in some paradigms can be harder to comprehend, but to date, no research has looked at which paradigm-specific language features impact comprehension. To this end, this study seeks to uncover which paradigm-specific features impactcomprehension and debugging of code or how multi-paradigm code might affect a developer's ability to do so. We present an exploratory empirical eye-tracking study to investigate 1) how developers classify the predominant paradigm in Python code and 2) how the paradigm affects their ability to debug Python code. The goal is to uncover if specific language features are looked at more often while classifying and debugging code with a predominant paradigm. Twenty-nine developers…
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Taxonomy
TopicsSoftware Engineering Research · Teaching and Learning Programming · Software Engineering Techniques and Practices
