Comparative Analysis of the Code Generated by Popular Large Language Models (LLMs) for MISRA C++ Compliance

TL;DR

This study compares how well popular large language models generate C++ code that complies with MISRA standards for safety-critical systems, revealing current limitations and potential improvements.

Contribution

It provides a systematic comparison of LLMs' ability to produce MISRA-compliant C++ code and highlights the need for further refinement for safety-critical applications.

Findings

None of the LLMs fully comply with MISRA C++ standards.

DeepSeek had the fewest violations among models.

ChatGPT can identify and fix violations more effectively.

Abstract

Safety-critical systems are engineered systems whose failure or malfunction could result in catastrophic consequences. The software development for safety-critical systems necessitates rigorous engineering practices and adherence to certification standards like DO-178C for avionics. DO-178C is a guidance document which requires compliance to well-defined software coding standards like MISRA C++ to enforce coding guidelines that prevent the use of ambiguous, unsafe, or undefined constructs. Large Language Models (LLMs) have demonstrated significant capabilities in automatic code generation across a wide range of programming languages, including C++. Despite their impressive performance, code generated by LLMs in safety-critical domains must be carefully analyzed for conformance to MISRA C++ coding standards. In this paper, I have conducted a comparative analysis of the C++ code generated by popular LLMs including: OpenAI ChatGPT, Google Gemini, DeepSeek, Meta AI, and Microsoft Copilot for compliance with MISRA C++. The study revealed that none of the evaluated LLMs generated MISRA-compliant code despite clear prompts, with DeepSeek showing the fewest violations and Meta AI the most. While all models could correct individual violations when explicitly instructed, only ChatGPT consistently identified and resolved all targeted rule violations across complete code snippets, whereas others achieved partial success. Overall, LLMs show promise as aids for initial code generation, but they are not yet dependable for producing fully MISRA-compliant code required in safety-critical domains.