An Eye Tracking Perspective on How Developers Rate Source Code Readability Rules

Peterson, Cole S.; Park, Kang-il; Baysinger, Isaac; Sharif, Bonita

doi:10.1109/asew52652.2021.00037

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Although the structural features indicate these findings, tracking students' gazes proved that time spent reading such splitting points corresponds to unreadable code. This is in line with the findings of [20] which also used eye‐tracking to show that nested blocks introducing nonlinearities decrease code readability.…”

Section: Resultssupporting

confidence: 91%

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Assessing code readability in Python programming courses using eye‐tracking

Segedinac,

Savić,

Zeljković

et al. 2023

Comp Applic In Engineering

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Code readability models are typically based on the code's structural and textual features, considering code readability as an objective category. However, readability is inherently subjective and dependent on the knowledge and experience of the reader analyzing the code. This paper assesses the readability of Python code statements commonly used in undergraduate programming courses. Our readability model is based on tracking the reader's eye movement during the while‐read phase. It uses machine learning (ML) techniques and relies on a novel set of features—observational features—that capture how the readers read the code. We experimented by tracking the eye movement of 90 undergraduate students while assessing the readability of 48 Python code snippets. We trained an ML model that predicts readability based on the collected observational data and the code snippet's structural and textual features. In our experiments, the XGBoost classifier trained using observational features exclusively achieved the best results (0.85 F‐measure). Using correlation analysis, we identified Python statements most affecting readability for undergraduate students and proposed implications for teaching Python programming. In line with findings for Java language, we found that constructs related to the code's size and complexity hurt the code's readability. Numerous comments also hindered readability, potentially due to their association with less readable code. Some Python‐specific statements (list comprehension, lambda function, and dictionary comprehension) harmed code readability, even though they were part of the curriculum. Tracking students' gaze indicated some additional factors, most notably nonlinearity introduced by if, for, while, try, and function call statements.

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Section: Resultssupporting

confidence: 91%

“…Existing approaches for predicting code readability rely on the human notion of readability (typically expressed on the Likert scale that captures annotators' opinions) as ground truth [5,6,20,25]. However, these approaches rely solely on code characteristics when predicting the human notion of code readability.…”

mentioning

confidence: 99%

Assessing code readability in Python programming courses using eye‐tracking

Segedinac,

Savić,

Zeljković

et al. 2023

Comp Applic In Engineering

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On Eye Tracking in Software Engineering

Grabinger,

Hauser,

Wolff

et al. 2024

SN COMPUT. SCI.

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Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap; it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies.

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An Eye Tracking Perspective on How Developers Rate Source Code Readability Rules

Cited by 2 publications

References 5 publications

Assessing code readability in Python programming courses using eye‐tracking

Assessing code readability in Python programming courses using eye‐tracking

On Eye Tracking in Software Engineering

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