Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts
Large Language Models (LLMs) are increasingly used for code smell detection tasks due to their ability to interpret program semantics. However, their reliability in this context remains poorly explored, particularly under varying prompt conditions where model predictions may be influenced by external cues rather than code characteristics. One such limitation is sycophancy bias, where models tend to align their outputs with user-provided assumptions instead of performing objective analysis. In this paper, we present the first systematic empirical study of sycophancy bias in LLM-based code smell detection. Using the MLCQ dataset, we evaluate how different prompt framings like confirmation b...
| Authors | Istiaq Ahmed Fahad, Kamruzzaman Asif, Md. Nurul Ahad Tawhid |
|---|---|
| Primary category | cs.SE |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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