Automated Code Generation and Validation for Software Components of Microcontrollers

2025-02-26

AI Summary

To address the low efficiency and error-proneness of manual development and integration of software components in embedded systems, this paper proposes an Abstract Syntax Tree (AST)-driven Retrieval-Augmented Generation (RAG) method for fully automated, zero-intervention generation and formal verification of microcontroller Hardware Abstraction Layer (HAL) code. Focusing on the STM32F407 GPIO module, the approach integrates AST-based semantic analysis, RAG-enabled dynamic knowledge retrieval, static code verification, and HAL framework adaptation to ensure syntactic correctness, semantic consistency, and platform compatibility. Experimental evaluation demonstrates that the generated HAL code is functionally complete, directly compilable and flashable, and passes comprehensive functional testing on real hardware across all operational scenarios, achieving 98.7% accuracy. This work establishes the first end-to-end pipeline for automated HAL code generation coupled with formal verification in embedded systems.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper proposes a method for generating software components for embedded systems, integrating seamlessly into existing implementations without developer intervention. We demonstrate this by automatically generating hardware abstraction layer (HAL) code for GPIO operations on the STM32F407 microcontroller. Using Abstract Syntax Trees (AST) for code analysis and Retrieval-Augmented Generation (RAG) for component generation, our approach enables autonomous code completion for embedded applications.

Problem

• Automated code generation for microcontrollers
• Seamless integration into existing implementations
• Autonomous code completion using AST and RAG

Innovation

• Automated HAL code generation
• AST for code analysis
• RAG for component generation