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Programmed differently? Testing for gender differences in Python programming style and quality on GitHub

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  • Brooke, Sian

Abstract

The underrepresentation of women in open-source software is frequently attributed to women’s lack of innate aptitude compared to men: natural gender differences in technical ability (Trinkenreich et al., 2021). Approaching code as a form of communication, I conduct a novel empirical study of gender differences in Python programming on GitHub. Based on 1,728 open-source projects, I ask if there is a gender difference in the quality and style of Python code measured in adherence to PEP-8 guidelines. I found significant gender differences in structure and how Python files are organized. While there is gendered variation in programming style, there is no evidence of gender difference in code quality. Using a Random Forest model, I show that the gender of a programmer can be predicted from the style of their Python code. The study concludes that gender differences in Python code are a matter of style, not quality.

Suggested Citation

  • Brooke, Sian, 2024. "Programmed differently? Testing for gender differences in Python programming style and quality on GitHub," LSE Research Online Documents on Economics 120988, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:120988
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    File URL: http://eprints.lse.ac.uk/120988/
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    More about this item

    Keywords

    gender; open-source software; computational methods; intersectional and feminist approaches; HCI; Early Career Fellowship; grant number ECF-2021-272; OUP deal;
    All these keywords.

    JEL classification:

    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General

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