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

2026-07-09 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Internet of Things (IoT) & Connected Devices · Depth: Advanced, quick

Summary

SmartHomeSecure is a prototype system designed for automated detection and repair of configuration errors in smart home YAML files, which are prone to syntax, formatting, and semantic logic issues. The system integrates lightweight program analysis with constraint-guided large language model (LLM) generation to address limitations of existing validators and general-purpose LLMs. SmartHomeSecure parses YAML files, identifies syntactic and common semantic errors, normalizes error context, applies deterministic auto-fixes for routine defects, and constructs constrained prompts to guide LLMs toward minimal and structurally valid repairs. Implemented as a modular web application, its repair pipeline was evaluated on 100 real-world Assistant YAML files with manually injected errors across five categories. Four models—gpt-oss-20b, gpt-oss-120b, llama-3.1-8b, and llama-3.3-70b—were tested, with three achieving 100% error detection accuracy and repair success rates ranging from 87% to 93%. Manual verification confirmed no hallucinated or incorrect repairs.

Key takeaway

For AI Engineers developing automation platforms, you should consider integrating domain-aware program analysis with constrained LLM generation for configuration error resolution. This approach, demonstrated by SmartHomeSecure's 87-93% repair success rates, significantly enhances reliability and reduces manual debugging efforts. Implement deterministic pre-processing and strict prompt constraints to ensure LLM outputs are accurate and structurally valid, minimizing the risk of incorrect automated repairs.

Key insights

Combining domain-aware program analysis with constrained generative AI effectively repairs smart home configuration errors.

Principles

• YAML configuration files are prone to diverse syntax and semantic errors.
• Domain-specific understanding is crucial for effective error diagnosis.
• Constrained LLM generation improves repair accuracy and validity.

Method

SmartHomeSecure parses YAML, detects errors, normalizes context, applies deterministic fixes, and uses constrained prompts to guide LLMs for minimal, valid repairs.

In practice

• Implement deterministic auto-fixes for routine configuration defects.
• Use constraint-guided prompting to prevent LLM hallucinations in code repair.

Topics

• Smart Home Automation
• YAML Configuration
• Large Language Models
• Program Analysis
• Error Detection
• Automated Repair

Best for: Research Scientist, AI Scientist, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.