Cited by 5Open Source Software InnovationsSoftware Engineering ResearchSoftware Engineering Techniques and Practices

The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index; none of the 54 sources its claims cite is checked for support yet.

“Write in English, Nobody Understands Your Language Here”: A Study of Non-English Trends in Open-Source Repositories

Open access
Bhuiyan, Masudul Hasan Masud, Kumar, Manish Kumar Bala, Staicu, Cristian-Alexandru·2026·Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering

Published version of a preprint

Abstract

The open-source software (OSS) community has historically been dominated by English as the primary language for code, documentation, and developer interactions. However, with growing global participation and better support for non-Latin scripts through standards like Unicode, OSS is gradually becoming more multilingual. This study investigates the extent to which OSS is becoming more multilingual, analyzing 9.14 billion GitHub issues, pull requests, and discussions, and 62,500 repositories across five programming languages and 30 natural languages, covering the period from 2015 to 2025. We examine six research questions to track changes in language use across communication, code, and documentation. We find that multilingual participation has steadily increased, especially in Korean, Chinese, and Russian. This growth appears not only in issues and discussions but also in code comments, string literals, and documentation files. While this shift reflects greater inclusivity and language diversity in OSS, it also creates language tension. The ability to express oneself in a native language can clash with shared norms around English use, especially in collaborative settings. Non-English or multilingual projects tend to receive less visibility and participation, suggesting that language remains both a resource and a barrier, shaping who gets heard, who contributes, and how open collaboration unfolds.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This large-scale study finds GitHub open-source communication and code are becoming more multilingual, with non-English issue/PR content rising 164% from 2015 to 2025 and Korean, Chinese, and Russian leading growth. Multilingualism increases inclusivity, but non-English and mixed-language repositories receive fewer comments, contributors, and stars, while English-dominant projects resolve issues more slowly.

Key findings

  • Non-English content in issues and pull requests rose from 4.2% in 2015 to 11.3% in 2025, a 164% relative increase; Korean usage increased 1706% and Chinese 120%, with Chinese, Korean, Japanese, Russian, and Vietnamese as the top non-English languages.
  • In code, non-English comments rose from 3.6% to 11.9% of files and string literals from 3.2% to 9.6%; identifiers stayed mostly English (non-English identifiers 0.14% to 0.28%, variables 0.09% to 0.28%). Repositories with non-English documentation rose from 3.7% to 13.0%.
  • Java had the highest multilingual code share: in 2025, 22.03% of Java files had non-English comments and 13.2% had non-English literals; Python had 17.1% comments and 13.3% literals, JavaScript 19.7% comments and 12.7% literals, while C# and TypeScript were lower (C# 10.2% comments; TypeScript 12.3% comments, 7.08% literals).
  • English-dominant repositories had significantly more comments, contributors, and stars than mixed and non-English repositories; median stars were 36, 12, and 2, respectively. Issue resolution medians were 7.17 days for English projects, 4.70 for mixed, and 3.39 for non-English; 193,000 repositories used multiple languages, and language-enforcement phrases such as “write in English” appeared 3,801 times.

Methods

  • Two pipelines: GHArchive for communication (9.14 billion messages from over 476 million repositories and 78.01 million developers, January 2015–May 2025; 1.7 billion retained natural-language data points) and the GitHub REST API for code patches (62,500 repositories across JavaScript, Python, TypeScript, Java, and C#; 385 patches per month, totaling 48,125 patches; 30 natural languages).
  • Language detection used Lingua v2.1.1 for messages (AUC 0.91, confidence threshold 0.9) and the Google Cloud Translation API for code elements; tree-sitter parsed identifiers, function/class names, literals, and comments. Statistical testing used Kruskal-Wallis H-tests and pairwise Mann-Whitney U tests with Bonferroni correction.

Limitations

  • Stated: language detection on short, informal, or code-mixed GitHub text is challenging, and strict confidence thresholds plus focus on non-Latin languages make estimates conservative lower bounds. Code analysis included only patches introducing new files over 500 characters; activity is limited to January 2015–May 2025; repository sampling and 90% language-dominance classification may miss less active or visible repositories; findings are limited to public GitHub and may not generalize to other platforms or private repositories.
  • Evident: the study is observational, so the engagement differences cannot establish causality; the authors note causality is hard to confirm and some explanations remain hypotheses. Language detection also excludes Latin-based languages, so trends for Spanish, French, and similar languages are not measured.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1145/3744916.3787766

Cites

63 of 63 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1 Overview of our methodology: data collection, language detection, and analysis across code and discussions.
Figure 1 Overview of our methodology: data collection, language detection, and analysis across code and discussions.CC-BY · From the PDF
Image 2
CC-BY · From the PDF
Image 3
CC-BY · From the PDF
Figure 2 Evaluation of language detection models: (a) ROC curve and threshold selection for Lingua ; (b) performance comparison with Google Translator on short code texts.
Figure 2 Evaluation of language detection models: (a) ROC curve and threshold selection for Lingua ; (b) performance comparison with Google Translator on short code texts.CC-BY · From the PDF
Figure 4 Monthly distribution of non-English natural languages in GitHub discussions from 2015 to 2025, highlighting the sustained presence of Chinese, Japanese, Russian, Korean, and Vietnamese, and the growing participation in Korean, Persian, and Vietnamese over time.
Figure 4 Monthly distribution of non-English natural languages in GitHub discussions from 2015 to 2025, highlighting the sustained presence of Chinese, Japanese, Russian, Korean, and Vietnamese, and the growing participation in Korean, Persian, and Vietnamese over time.CC-BY · From the PDF
Figure 5 Share of non-English content in code elements from 2015-2025, with comments and string literals showing the most growth.
Figure 5 Share of non-English content in code elements from 2015-2025, with comments and string literals showing the most growth.CC-BY · From the PDF
Figure 6 Percentage of repositories containing non-English documentation from 2015 to 2025.
Figure 6 Percentage of repositories containing non-English documentation from 2015 to 2025.CC-BY · From the PDF
Figure 7 Non-English content in source code by programming language from 2015 to 2025. (a) Percentage of files with non-English comments. (b) Percentage of files with nonEnglish string literals. Java, JavaScript, and Python exhibit the most significant multilingual growth over time.
Figure 7 Non-English content in source code by programming language from 2015 to 2025. (a) Percentage of files with non-English comments. (b) Percentage of files with nonEnglish string literals. Java, JavaScript, and Python exhibit the most significant multilingual growth over time.CC-BY · From the PDF
Figure 8 Distribution of English content across GitHub repositories in our dataset.
Figure 8 Distribution of English content across GitHub repositories in our dataset.CC-BY · From the PDF
Figure 9 Number of repositories by dominant discussion language. Most multilingual activity is concentrated in Englishdominant and mixed-language projects, with significant representation from Chinese, Japanese, and Korean repositories.
Figure 9 Number of repositories by dominant discussion language. Most multilingual activity is concentrated in Englishdominant and mixed-language projects, with significant representation from Chinese, Japanese, and Korean repositories.CC-BY · From the PDF
Figure 10 Total comments per repository by language category across contribution buckets (repository groups based on total commits). English-dominant projects lead in large buckets, while mixed-language projects show higher participation in smaller ones.
Figure 10 Total comments per repository by language category across contribution buckets (repository groups based on total commits). English-dominant projects lead in large buckets, while mixed-language projects show higher participation in smaller ones.CC-BY · From the PDF
Figure 11 Distribution of unique contributors per repository across language categories. English repositories have more contributor growth as project size increases, while nonEnglish repositories consistently have fewer contributors.
Figure 11 Distribution of unique contributors per repository across language categories. English repositories have more contributor growth as project size increases, while nonEnglish repositories consistently have fewer contributors.CC-BY · From the PDF
Figure 12 Distribution of GitHub stars per repository across language categories and contribution levels. Englishdominant repositories show higher visibility, especially as project size increases.
Figure 12 Distribution of GitHub stars per repository across language categories and contribution levels. Englishdominant repositories show higher visibility, especially as project size increases.CC-BY · From the PDF
Figure 13 Distribution of issue resolution time across language categories.
Figure 13 Distribution of issue resolution time across language categories.CC-BY · From the PDF

Verification

Claims the paper makes

90 Own result · 71 Own interpretation · 37 Sources not found · 7 Own method · 3 Not checked yet · 1 No basis given

Show all 115 claims

  • It may state two separate claims as one. 69
  • Probably not a substantial claim. 47
94 of 94
  • The paper relies on this without support.

    GitHub activity is representative of open-source software development.

    “To examine the role of multilingualism in open-source software development, we analyzed GitHub activity from January 2015 to May 2025.”
    “Open-source platforms contain multiple forms of natural language content.”
  • The paper relies on this without support.

    The sampled repositories are representative of the broader population of GitHub repositories.

    “this dataset covers a representative, temporally balanced sample of repositories rather than the full population.”
  • The paper relies on this without support.

    Non-English text in messages and code indicates multilingual participation.

    “The open-source software (OSS) community has historically been dominated by English as the primary language for code, documentation, and developer interactions.However, with growing global participation and better support for non-Latin scripts through standards like Unicode, OSS is gradually becoming more multilingual.”
  • The paper relies on this without support.

    South Korea represents less than 1% of the global population.

    “despite South Korea representing less than 1% of the global population.”
  • The paper relies on this without support.

    The manually annotated samples used to evaluate language detection are representative of the full dataset.

    “We evaluated the classifier's performance using a manually annotated sample of messages”
    “We empirically confirmed this limitation using a manually annotated sample of 1,000 short texts extracted from code.”
  • The paper relies on this without support.

    Random sampling in GitHub data tends to overrepresent JavaScript and Python.

    “Because random sampling in GitHub data tends to overrepresent JavaScript and Python, we applied stratified sampling to maintain balanced representation.”
  • The paper relies on this without support.

    Many schools teach Python as the first programming language.

    “Many schools teach Python as the first programming language, so students often write comments or literals in their own language.”
  • The paper relies on this without support.

    Students often write comments or literals in their own language.

    “Many schools teach Python as the first programming language, so students often write comments or literals in their own language.”
  • The paper relies on this without support.

    C# use remains tied to Microsoft and enterprise settings.

    “C# use remains tied to Microsoft and enterprise settings.”
  • The paper relies on this without support.

    Much of the current research on developer communication has been conducted exclusively on English-language data.

    “Much of the current research on developer communication, whether focused on toxicity [53,72,73], sexism [80], politeness [21], or emotional tone [54], has been conducted exclusively on English-language data.”
  • The paper relies on this without support.

    Automation is central to modern workflows, from issue triage to documentation bots.

    “Automation is central to modern workflows, from issue triage to documentation bots [30-32, 70, 89].”
  • The paper relies on this without support.

    Larger repositories naturally attract more comments, stars, and contributors.

    “Larger repositories naturally attract more comments, stars, and contributors, so comparing projects within similar activity levels provides a clearer picture of how language influences engagement.”
  • The paper relies on this without support.

    GitHub comments are central to issue triage, bug reporting, feature discussion, and community feedback.

    “Since GitHub comments are central to issue triage, bug reporting, feature discussion, and community feedback, lower comment volume may hinder transparent collaboration or discourage new contributors from engaging.”
  • The paper relies on this without support.

    Stars reflect perceived value or utility and are shaped by visibility.

    “Unlike contributor count, which reflects actual participation, stars reflect perceived value or utility and are shaped by visibility.”
  • The paper relies on this without support.

    Issue resolution time is a key indicator of project responsiveness and maintenance quality.

    “Issue resolution time is a key indicator of project responsiveness and maintenance quality.”
  • The paper relies on this without support.

    Latin-based languages share vocabulary that often confuses detectors on short text.

    “We restrict analysis to non-Latin languages because Latin-based languages (e.g., English, Spanish, French) share vocabulary that often confuses detectors on short text.”
  • The paper relies on this without support.

    Separating small edits or mixed-language content within the same file is difficult for reliable code parsing.

    “As our goal is to study longitudinal change, separating small edits or mixed-language content within the same file is difficult for reliable code parsing, which further motivates focusing on newly added files.”
  • The paper relies on this without support.

    Behavior in private repositories may differ.

    “Finally, our analysis covers only public repositories; behavior in private repositories may differ.”
  • The paper relies on this without support.

    English remains the lingua franca of open source.

    “English remains the lingua franca of open source”
  • The paper relies on this without support.

    Language and economics are largely overlooked in the literature.

    “they are largely overlooked in the literature”
  • The paper relies on this without support.

    Countries like India and Pakistan have multiple official languages, yet English is widely used as the working lingua franca.

    “Countries like India and Pakistan have multiple official languages, yet English is widely used as the working lingua franca.”
21 of 21

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

1 of 1

Citations

Cites

Show all 56 references

Research works printed but not linked: 8

  1. Wafaa Alsaggaf H Baaqeel S Alkhuraiji and Hani Brdesee. 2022. The Inclusion of Python as Introductory Computer Programming in the Preparatory Year of Higher Education: Modeling for Students’ Perceptions. IJCSNS International Journal of Computer Science and Network Security 22 3 (2022) 565–574.
  2. Brett A Becker. 2019. Parlez-vous Java? Bonjour La Monde!= Hello World: Barriers to Programming Language Acquisition for Non-Native English Speakers.. In PPIG.
  3. 10.5555/2390374.2390382 · DOI
  4. Jacopo Bregolin. 2022. Communication effort and the cost of language: Evidence from stack overflow. Technical Report.
  5. Bogdan-Mihai Buzatu. 2025. Data hound: Analysing non-English data smells in large code datasets. Ph. D. Dissertation. Delft University of Technology.
  6. Philip Guo. 2014. Python Is Now the Most Popular Introductory Teaching Language at Top U.S. Universities — Communications of the ACM. https://cacm.acm.org/blogcacm/python-is-now-the-most-popular-introductory-teaching-language-at-top-u-s-universities/.
  7. Marco Ortu, Giuseppe Destefanis, Mohamad Kassab, Steve Counsell, Michele Marchesi, and Roberto Tonelli. 2015. Would you mind fixing this issue? an empirical analysis of politeness and attractiveness in software developed using agile boards. In International conference on Agile software development. Springer, 129–140.
  8. 10.5555/2486788.2486931 · DOI

Cited by

Sorted by citations

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Metadata

Authors
Bhuiyan, M. H. M., Kumar, M. K. B., & Staicu, C.-A.
Affiliations
Helmholtz Center for Information Security (DE)
Venue
Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering
Publisher
ACM
Published
2026-04-12
Pages
2415-2427
ISSN
0270-5257 (ISSN-L)
OpenAlex
W7131318001
Cited by
5
References
56
Citations by year
2026: 5
FWCI
31.05
Type
conference paper
Language
en
Open access
Locations
  • ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
Keywords
open source software, multilingualism, linguistic diversity, GitHub, code comments, documentation, Korean language, Chinese, Russian, language barriers, inclusivity, developer engagement, Unicode

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