AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical debt in AI-generated software, revealing that AI does not eliminate flaws but rather introduces a distinct machine signature of defects. Our multi-scale analysis, spanning single-file algorithmic tasks and complex, agent generated systems, identifies a fundamental Reasoning-Complexity Trade-off: as models become more capable, they generate increasingly bloated and coupled code. This architectural decay is so pronounced that we establish a Volume-Quality Inverse La...
| Authors | Yuecai Zhu, Nikolaos Tsantalis, Peter C. Rigby |
|---|---|
| Primary category | cs.SE |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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