mlsanityData to 14 Oct 2026

Assessing LLM code generation quality through path planning tasks

arXiv 2504.21276 ↗First posted 30 Apr 2025cs.SE cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 30 Apr 2025.

Builds on

9 of 22 references.

References · 9

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  3. 2024A Survey on Large Language Models for Code Generationcited by 658
  4. 2023Model evaluation for extreme riskscited by 177
  5. 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 107
  6. 2024SWE-Bench+: Enhanced Coding Benchmark for LLMscited by 99
  7. 2024Bugs in Large Language Models Generated Code: An Empirical Studycited by 60
  8. 2024How Beginning Programmers and Code LLMs (Mis)read Each Othercited by 31
  9. 2022A Hazard Analysis Framework for Code Synthesis Large Language Modelscited by 22

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Citing papers per year: 2026: 2.

2 papers.