Authors: Ziyao Zhang, Yanlin Wang, Chong Wang, Jiachi Chen, Zibin Zheng
Abstract: Code generation aims to automatically generate code from input requirements,
significantly enhancing development efficiency. Recent large language models
(LLMs) based approaches have shown promising results and revolutionized code
generation task. Despite the promising performance, LLMs often generate
contents with hallucinations, especially for the code generation scenario
requiring the handling of complex contextual dependencies in practical
development process. Although previous study has analyzed hallucinations in
LLM-powered code generation, the study is limited to standalone function
generation. In this paper, we conduct an empirical study to study the
phenomena, mechanism, and mitigation of LLM hallucinations within more
practical and complex development contexts in repository-level generation
scenario. First, we manually examine the code generation results from six
mainstream LLMs to establish a hallucination taxonomy of LLM-generated code.
Next, we elaborate on the phenomenon of hallucinations, analyze their
distribution across different models. We then analyze causes of hallucinations
and identify four potential factors contributing to hallucinations. Finally, we
propose an RAG-based mitigation method, which demonstrates consistent
effectiveness in all studied LLMs. The replication package including code,
data, and experimental results is available at
https://github.com/DeepSoftwareAnalytics/LLMCodingHallucination
Source: http://arxiv.org/abs/2409.20550v1
