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

• URL: http://arxiv.org/abs/2607.06748v1
• Date: Tue, 07 Jul 2026 19:29:10 GMT
• Title: SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models
• Abstract summary: This paper presents SmartHomeSecure, a prototype for automated detection and repair of configuration errors.<n>The system is implemented as a modular web application with four layers: UI Shell, Feature Orchestrator, Domain Engine, and Integration Layer.<n>Three models achieved 100% error detection accuracy, with success rates ranging from 87% to 93%.
• Score: 12.096717905785779
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents [83.37969790806069]
  LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it.<n>We present Agent DebuggerX, an open-source framework that organizes debug as a closed loop of Detect, Attribute, Recover, and Rerun.<n>At its core, Deep Debugger performs multi-turn root-cause diagnosis through global trajectory understanding.
  arXiv  Detail & Related papers  (2026-07-21T06:21:13Z)
• Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs [0.11744028458220425]
  This paper presents a human-in-the-loop framework for identifying and repairing semantic errors automatically.<n>It combines a fine-tuned Small Language Model (SLM) with a domain knowledge graph encoding physical compatibility rules between system elements.<n>We demonstrate the framework using the vehicle systems domain, where the knowledge graph captures the relationships between the mechanical, electrical, fluid, and signal interfaces.
  arXiv  Detail & Related papers  (2026-06-22T14:20:11Z)
• PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices [22.432182416621917]
  textscPracRepair is a fully automated program repair framework inspired by human-like debug practices.<n>textscPracRepair consistently outperforms state-of-the-art baselines.<n>textscPracRepair generalizes effectively to RWB (Real-World Bugs)
  arXiv  Detail & Related papers  (2026-06-16T07:18:37Z)
• DeepFix: Debugging and Fixing Machine Learning Workflow using Agentic AI [0.0]
  DeepFix is a tool for automated testing of the entire machine learning pipeline using an agentic AI framework.<n>Our testing approach first leverages Deepchecks to test the ML software for any potential bugs, and thereafter, uses an agentic AI-based approach to generate a detailed bug report.<n>This includes a ranking, based on the severity of the found bugs, along with their explanations, which can be interpreted easily by any non-data science experts.
  arXiv  Detail & Related papers  (2026-03-14T20:00:16Z)
• ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement [57.98138819417949]
  We propose ErrorLLM, a framework that explicitly models text-to- querying.<n>We show that ErrorLLM achieves the most significant improvements over backbone initial generation.<n>ErrorLLM addresses both sides by high detection F1 score while maintaining refinement effectiveness.
  arXiv  Detail & Related papers  (2026-03-04T05:27:20Z)
• InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration [71.18377595277018]
  Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose.<n>We present InspectCoder, the first agentic program repair system that empowers LLMs to actively conduct dynamic analysis via interactive debugger control.
  arXiv  Detail & Related papers  (2025-10-21T06:26:29Z)
• Repairing Regex Vulnerabilities via Localization-Guided Instructions [6.033257307910245]
  Regular expressions (regexes) expose systems to regular expression denial of service (ReDoS)<n>Current approaches, however, are hampered by a trade-off.<n>We introduce a hybrid framework, localized repair (LRR), designed to harness generalization while enforcing reliability.
  arXiv  Detail & Related papers  (2025-10-10T06:15:43Z)
• Where LLM Agents Fail and How They can Learn From Failures [62.196870049524364]
  Large Language Model (LLM) agents have shown promise in solving complex, multi-step tasks.<n>They amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions.<n>Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way.<n>We introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations.
  arXiv  Detail & Related papers  (2025-09-29T18:20:27Z)
• Towards Automated Error Discovery: A Study in Conversational AI [48.735443116662026]
  We introduce Automated Error Discovery, a framework for detecting and defining errors in conversational AI.<n>We also propose SEEED (Soft Clustering Extended-Based Error Detection), as an encoder-based approach to its implementation.
  arXiv  Detail & Related papers  (2025-09-13T14:53:22Z)
• Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs [79.74676890436174]
  We present an APR tool for Dafny that uses formal specifications as oracles for fault localization and repair.<n>We localize faults through a series of steps, which include using Hoare logic to determine the state of each statement within the program.<n>Our tool achieves 89.6% fault localization coverage and GPT-4o mini yields the highest repair success rate of 74.18%.
  arXiv  Detail & Related papers  (2025-07-04T15:36:12Z)
• LecPrompt: A Prompt-based Approach for Logical Error Correction with CodeBERT [28.711745671275477]
  LecPrompt is a prompt-based approach to localize and repair logical errors. It harnesses the capabilities of CodeBERT, a transformer-based large language model trained on code. For Python, LecPrompt achieves a noteworthy 74.58% top-1 token-level repair accuracy. In Java, LecPrompt delivers a 69.23% top-1 token-level repair accuracy.
  arXiv  Detail & Related papers  (2024-10-10T01:56:04Z)
• A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [39.11825182386288]
  FixAgent is an end-to-end framework for unified debug through multi-agent synergy. It significantly outperforms state-of-the-art repair methods, fixing 1.25$times$ to 2.56$times$ bugs on the repo-level benchmark, Defects4J.
  arXiv  Detail & Related papers  (2024-04-26T04:55:35Z)
• GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence [64.95492752484171]
  We present GenAudit -- a tool intended to assist fact-checking LLM responses for document-grounded tasks.<n>GenAudit suggests edits to the LLM response by revising or removing claims that are not supported by the reference document, and also presents evidence from the reference for facts that do appear to have support.<n> Comprehensive evaluation by human raters shows that GenAudit can detect errors in 8 different LLM outputs when summarizing documents from diverse domains.
  arXiv  Detail & Related papers  (2024-02-19T21:45:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.