[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85656-en":3,"doc-seo-85656-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85656,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","From Codebases to LLMs Non-Inclusive Naming in Linux Foundation Repositories","Since 2020, the Linux Foundation and the Inclusive Naming Initiative (INI) have promoted replacing non-inclusive terms in open source, but evidence on long-term uptake and downstream effects has been limited. This paper introduces NISCAN, a multilingual static-analysis framework that detects non-inclusive terminology using INI vocabulary and performs the first ecosystem-scale study across 461 Linux Foundation repositories. Results show a 47% decline since 2020, while 62.7% of repositories still retain Tier-1 non-inclusive identifiers. Additional findings identify stronger predictors of inclusiveness and evaluate how large language models reconstruct legacy identifiers, highlighting terminology residue as linguistic technical debt that can amplify biases and risk ethically sourced code generation.","From Codebases to LLMs: Non-Inclusive Naming in Linux Foundation Repositories  \nHonghao Tan∗ , Md Nafiu Rahman†, Shin Hwei Tan∗  \n∗ Concordia University, Montreal, Canada  \n[honghao.tan@mail.concordia.ca](honghao.tan@mail.concordia.ca), [shinhwei.tan@concordia.ca](shinhwei.tan@concordia.ca)  \n†Brac University, Dhaka, Bangladesh  \n[nafiu.rahman@bracu.ac.bd](nafiu.rahman@bracu.ac.bd)  \narXiv :2607 .02772v2 [ cs . SE] 12 Jul 2026  \nAbstract—Since 2020, the Linux Foundation and the multiorganization Inclusive Naming Initiative (INI) have encouraged open-source projects to replace non-inclusive terms such as master/slave and whitelist/blacklist. Although these recommendations have been widely adopted, there is limited empirical evidence on their long-term adoption across Linux Foundation (LF) projects or their implications for AI-assisted software development. In this paper, we present NISCAN, a multilingual static-analysis framework that detects non-inclusive terminology across source code and related software artifacts using the INI vocabulary. Using NISCAN, we conduct the first ecosystem-scale study of inclusive naming across 461 Linux Foundation repositories. Our analysis shows that non-inclusive terminology has declined by approximately 47% since 2020, yet adoption remains incomplete: 62.7% of repositories still contain at least one Tier-1 non-inclusive identifier, while most remaining terminology resides outside source code in documentation, comments, configuration files, and other software artifacts. We further show that repository size, programming language, project functionality, and ecosystem are stronger predictors of term inclusiveness in LF repositories rather than foundation governance. To examine the implications for AI-assisted software development, we conduct a case study evaluating whether large language models (LLMs) can reconstruct legacy non-inclusive identifiers from surrounding program context. The results show that historical naming decisions remain embedded in model predictions even after identifiers have been renamed. Overall, our study findings provide the first ecosystem-scale assessment of inclusive naming adoption within the Linux Foundation and highlight the importance of addressing terminology residue to support responsible naming and ethically sourced code generation.  \nIndex Terms—Inclusive naming, non-inclusive terminology, mining software repositories, open-source governance, identifier naming.  \nI. INTRODUCTION  \nInclusive naming has become an important software engineering practice for improving the accessibility, professionalism, and inclusiveness of software systems [1] . In response to growing recognition that terms such as master/slave, whitelist/blacklist, and other historically established identifiers may be perceived as non-inclusive or offensive, software communities initiated one of the largest coordinated terminology migrations in the history of open-source software. In 2020, GitHub changed the default branch name for new repositories from master to main, while Linux Foundation (LF), together with the Cloud Native Computing Foundation, IBM, Red Hat, Cisco, and VMware, co-founded the Inclusive Naming  \nInitiative (INI) [2] to promote the adoption of more inclusive terminology across software systems. The movement has since expanded beyond community-driven recommendations. On 19 June 2025, the IEEE Standards Association approved IEEE Std 3400-2025 [3], providing the first international standard for evaluating and adopting inclusive terminology in engineering systems. These industry and standards efforts show that inclusive naming is now recognized as an important software engineering practice rather than merely a language preference. Despite coordinated efforts by industry and open-source communities, inclusive naming remains an ongoing software engineering challenge rather than a completed transition.  \nWhile inclusive naming guidelines have been widely adopted by organizations and pro","cbCaitT3IUaKVauH","https://ap.wps.com/l/cbCaitT3IUaKVauH","pdf",678659,1,12,"English","en",105,"# Introduction\n## Motivation and background\n## Research gap and importance\n## Linguistic technical debt and AI implications","[{\"question\":\"What is NISCAN and what does it detect?\",\"answer\":\"NISCAN is a multilingual static-analysis framework that detects non-inclusive terminology across source code and related software artifacts using the INI vocabulary.\"},{\"question\":\"What did the ecosystem-scale study across 461 Linux Foundation repositories find?\",\"answer\":\"Non-inclusive terminology declined by about 47% since 2020, but adoption remains incomplete: 62.7% of repositories still contain at least one Tier-1 non-inclusive identifier, with much remaining terminology outside source code.\"},{\"question\":\"How do large language models affect legacy non-inclusive naming?\",\"answer\":\"In the case study, LLMs often reconstruct legacy non-inclusive identifiers from surrounding program context, and naming decisions remain embedded in model predictions even after identifiers are 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