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Assessing LLM code generation quality through path planning tasks

Software Engineering 2025-05-01 v1 Artificial Intelligence

Abstract

As LLM-generated code grows in popularity, more evaluation is needed to assess the risks of using such tools, especially for safety-critical applications such as path planning. Existing coding benchmarks are insufficient as they do not reflect the context and complexity of safety-critical applications. To this end, we assessed six LLMs' abilities to generate the code for three different path-planning algorithms and tested them on three maps of various difficulties. Our results suggest that LLM-generated code presents serious hazards for path planning applications and should not be applied in safety-critical contexts without rigorous testing.

Keywords

Cite

@article{arxiv.2504.21276,
  title  = {Assessing LLM code generation quality through path planning tasks},
  author = {Wanyi Chen and Meng-Wen Su and Mary L. Cummings},
  journal= {arXiv preprint arXiv:2504.21276},
  year   = {2025}
}
R2 v1 2026-06-28T23:16:11.797Z