An LLM-Augmented Framework for Embedded Firmware Development with Automated Validation and Hardware-in-the-Loop Testing

Abstract

The application of Large Language Models (LLMs) to embedded firmware development presents unique challenges: domain-specific register-level programming, stringent resource constraints, and the absence of structured validation mechanisms. Existing approaches using direct LLM-assisted coding suffer from hallucinated library functions, incorrect register configurations, and low compilation success rates, particularly for resource-constrained microcontrollers. This paper presents a three-layer framework that integrates DeepSeek-based code generation with an automated cross-compilation validation pipeline and hardware-in-the-loop (HIL) testing. The framework comprises an LLM integration layer with a local protocol proxy bridge, a three-stage code validation pipeline (static analysis, cross-compilation, and HIL testing), and a domain-specific prompt engineering module. Empirical evaluation across five benchmark tasks on ESP32-S3 and STM32F103 platforms demonstrates that the framework reduces development time by 53.7% on average compared to manual development (p < 0.001, Cohen's $\mathrm{d} = 2.34)$, while achieving 89.2% functional correctness—a 16.7 percentage point improvement over direct LLM-assisted coding $(\mathrm{p}<0.01$, Cohen's $\mathrm{d}=1.18)$. Error analysis reveals that hallucinated API calls are reduced by 77.2% and logic/timing errors by 74.1%. Compared to the recently proposed AutoEmbed system, our framework achieves comparable end-toend success rates (89.2% vs. 86.5%) while providing explicit cross-compilation and HIL validation stages absent from AutoEmbed's auto-programming approach. The framework leverages DeepSeek-V4-Flash for code generation, achieving substantial cost reduction compared to GPT-4-based alternatives based on published API pricing, making it economically viable for iterative embedded development workflows.