SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models

2026-07-07

AI Summary

This work addresses the critical challenges posed by syntactic, formatting, and logical errors in YAML-based smart home configurations, which frequently lead to automation failures and security vulnerabilities. Existing tools lack domain awareness and robust repair capabilities. To overcome these limitations, the authors propose a novel paradigm that integrates lightweight program analysis with constraint-guided large language models. Their end-to-end, modular repair system leverages YAML parsing, context normalization, deterministic repair strategies, and constraint-aware prompt engineering. Evaluated on 100 real-world configuration files, the approach achieves 100% error detection and a repair success rate of 87%–93%. Manual validation confirms the absence of hallucinations or invalid fixes, demonstrating substantial improvements in both accuracy and safety of configuration repairs.

Abstract

Smart home automation platforms increasingly rely on user-authored YAML configuration files to define device behaviors, but these files are prone to syntax, formatting, and semantic logic errors that can cause automation failures and safety risks. Existing YAML validators, static analysis tools, and general-purpose large language models offer limited support for end-to-end diagnosis and repair because they lack domain-specific understanding and validated correction workflows. This paper presents SmartHomeSecure, a prototype for automated detection and repair of Home Assistant configuration errors using lightweight program analysis and constraint-guided large language model generation. SmartHomeSecure parses YAML files, detects syntactic and common semantic errors, normalizes error context, applies deterministic auto-fixes for routine defects, and constructs constrained prompts that guide LLMs toward minimal and structurally valid repairs. The system is implemented as a modular web application with four layers: UI Shell, Feature Orchestrator, Domain Engine, and Integration Layer. Its repair pipeline was evaluated on 100 real-world Home Assistant YAML files with manually injected errors across five categories: syntax/parsing, indentation, mapping, sequence, and scalar quoting errors. Four models were tested: gpt-oss-20b, gpt-oss-120b, llama-3.1-8b, and llama-3.3-70b. Results show that three models achieved 100% error detection accuracy, with repair success rates ranging from 87% to 93%. Manual verification found no hallucinated or incorrect repairs among successful outputs. These findings suggest that combining domain-aware program analysis with constrained generative AI is a feasible approach for improving the reliability and usability of smart home configuration repair.

Problem

• Smart Home
• YAML Configuration
• Configuration Errors
• Error Detection
• Error Repair

Innovation

• constraint-guided LLM
• automated configuration repair
• YAML error detection
• domain-aware program analysis
• smart home security