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Does AI-Assisted Development Homogenize Software? An Empirical Study of Code Similarity, Quality, and Evolution

Luciano Marchezan, Romain Lefeuvre, Kevin Delcourt, Benoît Combemale, Houari A. Sahraoui · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24093 · cited by 0 works (OpenAlex) · open-access PDF

Arabic

وتتطور تطور تطوير البرمجيات المساعدة من برنامج المساعدة الذكية إلى أنظمة المساعدة التي تعمل بشكل مستقل في مهام الهندسة البرمجية، لكن تأثيرات هذا التغيير لا تزال غير واضحة. نحن نقدم دراسة تجريبية عن 50 مشروعاً طويلة الأمد من Java وPython بأشكال مفتوحة عبر ثلاث مراحل: قبل-LLM، على الأرجح-LLM، وتحديد تطوير المساعدة الذكية. باستخدام مقاطع الفيديو من إرث البرمجيات، ونحن نتحقق من نوعية هذا التغيير من نوعية البرمجيات، ونوعية البرمجيات، ونوعية البرمجيات، وتطور البرمجيات. يتم تقييم المساواة عن طريق التقييم الجزيئي من خلال تقييمات

Machine translation (Meta M2M100); the English original follows.

Abstract

AI-assisted software development is evolving from interactive LLM assistance toward agentic systems that autonomously perform software engineering tasks, yet the effects of this shift remain insufficiently understood. We present an empirical study of 50 long-lived Java and Python open-source projects across three eras: pre-LLM, potentially LLM-assisted, and detected agentic development. Using snapshots from Software Heritage, we examine code similarity, software quality, and software evolution. Similarity is measured semantically through embedding-based cosine similarity and syntactically through CodeBLEU*. Quality is assessed with SonarQube metrics covering maintainability, complexity, reliability, and security. Evolution is characterized by annualized growth rates of LOC, functions, classes, files, and statements, and by commit activity in the three months preceding each snapshot. Results show no consistent evidence of increased similarity as semantic and syntactic similarity decreased significantly in Java for both the LLM and agentic eras relative to the pre-LLM era, while Python showed no significant differences. Software quality remained largely stable, although the agentic era showed significantly lower code-smell and SQALE index densities. Structural growth was generally lower in the agentic era, particularly for files, whereas development activity increased moderately in Java, especially in commits and added lines. These findings challenge the assumption that LLM- and agent-assisted development necessarily homogenizes code, degrades quality, or accelerates growth.

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Topics: Python (programming language), Java, Commit, Computer science, Software development, Software quality

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