Quality Assessment of LLM-Generated Multi-Language Systems: Comparative Analysis Using Design Smells

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Shahrukh Ansari, Harinath Kuruva, Salman Abdul Moiz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9407347/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Large Language Models (LLMs) are increasingly used to generate source code, including software that integrates multiple programming languages. While prior research has examined correctness and security aspects of LLM-generated code, little is known about its structural quality in multi-language settings, particularly with respect to cross-language design smells. Multi-language systems, such as those based on the Java Native Interface (JNI), introduce additional architectural complexity due to the need for coordination across language boundaries.In this paper, we present an exploratory empirical study investigating the prevalence and distribution of multi-language design smells in LLM-generated codes. We design a set of 80 JNI-based programming tasks covering 16 multi-language smells and generate implementations using four state-of-the-art LLMs: ChatGPT-5.2 , Claude Sonnet 4.6 , Gemini 3.1 pro , and Qwen3-Coder . The generated code is analyzed using a rule-based detection framework to identify the presence and distribution of multli-language design smells.Our results show that multi-language design smells are prevalent across all models, with up to 60% of generated implementations containing at least one smell. We observe significant variation across both models and smell types. Some smells occur consistently across all LLMs, suggesting shared structural tendencies, while others exhibit strong model-dependent behavior. These findings indicate that structural deficiencies in LLM-generated multi-language code are systematic and not uniformly distributed.Overall, this study highlights that architectural quality remains an underexplored dimension of LLM-based code generation, particularly in multi-language contexts. The results suggest that existing evaluation practices, which focus primarily on correctness, may overlook important structural issues. We argue for the need to incorporate multi-language structural quality assessment into future evaluation frameworks for LLM-generated codes. Code smells design smells Multi-language systems Multi-language design smells Code smell Detection Technique change-proneness fault-proneness Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9407347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625784295,"identity":"65e3f9ce-7515-4c1f-ac59-008dbb43837c","order_by":0,"name":"Md. 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While prior research has examined correctness and security aspects of LLM-generated code, little is known about its structural quality in multi-language settings, particularly with respect to cross-language design smells. Multi-language systems, such as those based on the Java Native Interface (JNI), introduce additional architectural complexity due to the need for coordination across language boundaries.In this paper, we present an exploratory empirical study investigating the prevalence and distribution of multi-language design smells in LLM-generated codes. We design a set of 80 JNI-based programming tasks covering 16 multi-language smells and generate implementations using four state-of-the-art LLMs: \u003cem\u003eChatGPT-5.2\u003c/em\u003e, \u003cem\u003eClaude Sonnet 4.6\u003c/em\u003e, \u003cem\u003eGemini 3.1 pro\u003c/em\u003e, and \u003cem\u003eQwen3-Coder\u003c/em\u003e. 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