Istiaq Ahmed Fahad

Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts

This paper presents the first systematic empirical study of sycophancy bias in LLM-based code smell detection, and proposes Evidence-Guided Debiasing Prompting (EGDP), a structured prompting strategy that enforces evidence-first reasoning and reduces decision instability and improves robustness.