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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

Korean

AI 에이전트 보조 소프트웨어 개발은 인터랙티브 LLM 지원에서 자율적으로 소프트웨어 엔지니어링 작업을 수행하는 에이전트 시스템으로 진화하고 있지만이 변화의 효과는 여전히 충분히 이해되지 않습니다. 우리는 50 개의 오랫동안 살아있는 Java 및 Python 오픈 소스 프로젝트를 세 시대에 걸쳐 소개합니다 : Pre-LLM, 잠재적으로 LLM 보조 및 감지 된 에이전트 개발. 소프트웨어 유산의 스냅샷을 사용하여이 변화는 코드 유사성, 소프트웨어 품질 및 소프트웨어 진화를 검사합니다. 유사성은 삽입 기반의 코시인 유사성과 CodeBLEU*을 통해 합법적으로 측정됩니다. 품질은 SonarQube 홈 메트릭스가 유지 보수, 복잡성, 신

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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