[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84945-en":3,"doc-seo-84945-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84945,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","SmartHomeSecure Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models","SmartHomeSecure addresses failures and safety risks caused by errors in user-authored YAML configuration files used by smart home automation platforms, especially Home Assistant. Existing YAML validators and generic LLMs lack domain-specific semantics and end-to-end correction workflows, limiting reliable diagnosis and repair. SmartHomeSecure parses YAML, detects syntax and common semantic issues, normalizes error context, applies deterministic fixes, and generates minimal structurally valid repairs via constraint-guided prompts. A modular web pipeline evaluates accuracy and repair success across injected error categories.","SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models  \nYizhi Wang 1, Xinghua Gao1*, Reachsak Ly2, Alireza Shojaei 1  \n1 Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA  \n2 School of Technology, Eastern Illinois University, Charleston, Illinois, United States  \n* Corresponding author, [email: xinghua@vt.edu](email: xinghua@vt.edu)  \nAbstract  \nSmart 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 asa 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.  \n1. Introduction  \nSmart homes have become a central part of modern living (Schulz & Scilla, 2024) . Open-source platforms like Home Assistant, known for local control and high customizability, now serve over two million households worldwide (Home Assistant, 2025) . These platforms commonly use YAML files to define complex automation behaviors.  \nHowever, the flexibility of YAML configurations also makes them error prone (Anik et al., 2024) . Common syntax errors include indentation and formatting issues. Semantic logic errors include invalid triggers and conflicting rules. Such errors can cause automation failures. In worse cases, they lead to abnormal device behavior, which harms user experience and may even compromise safety (Yu et al., 2024) .  \nDespite how common these errors are, no effective tools exist to automatically diagnose and fix them (Anik et al., 2024) . Basic syntax checkers cannot understand the domain-specific semantics of smart home configurations, nor can they handle logical errors. Even advanced static analysis tools designed specifically for IoT automation rules have been shown to lack the necessary semantic understanding and common-sense reasoning (Quantrill et al., 2026) . A recent study by Anik et al. (2024) examined 190 real-world configuration discussions from the Home Assistant community and found that 68% of the issues involved debugging. Existing tools detected at most 14 out of 129 buggy cases and fixed none.  \nUsers often resort to trial-and-error or community forums for troubleshooting. This process is time-consuming and often ineffective. Even general-purpose large language models do not perform well on this task (Ghaleb & Rathnayake, 2025) . Their outputs lack a","cbCaiejHGXHrz2ND","https://ap.wps.com/l/cbCaiejHGXHrz2ND","pdf",592291,1,20,"English","en",105,"# Introduction\n## Problem and Motivation\n## Limitations of Existing Tools\n## Proposed Approach and Contributions","[{\"question\":\"What types of errors does SmartHomeSecure detect in Home Assistant YAML files?\",\"answer\":\"It targets syntax/parsing errors, indentation issues, mapping and sequence problems, and scalar quoting errors. It also identifies common semantic logic errors such as invalid triggers and conflicting rules.\"},{\"question\":\"How does SmartHomeSecure reduce hallucinations during LLM-based repair?\",\"answer\":\"It combines lightweight program analysis with constraint-guided prompt generation, injecting precise contextual structure into prompts. The repair workflow also applies deterministic auto-fixes for routine defects before generation.\"},{\"question\":\"How was SmartHomeSecure evaluated, and what were the key results?\",\"answer\":\"The system was tested on 100 real-world Home Assistant YAML files with manually injected errors across five categories. Three evaluated models achieved 100% error detection accuracy, with repair success rates between 87% and 93%, and successful outputs showed no hallucinated or incorrect repairs upon manual verification.\"}]",1784199624,50,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"smarthomesecure-automated-detection-and-repair-of-smart-home-configuration-errors-using-large-language-models","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/smarthomesecure-automated-detection-and-repair-of-smart-home-configuration-errors-using-large-language-models/84945/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What types of errors does SmartHomeSecure detect in Home Assistant YAML files?","Question",{"text":75,"@type":76},"It targets syntax/parsing errors, indentation issues, mapping and sequence problems, and scalar quoting errors. It also identifies common semantic logic errors such as invalid triggers and conflicting rules.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SmartHomeSecure reduce hallucinations during LLM-based repair?",{"text":80,"@type":76},"It combines lightweight program analysis with constraint-guided prompt generation, injecting precise contextual structure into prompts. The repair workflow also applies deterministic auto-fixes for routine defects before generation.",{"name":82,"@type":73,"acceptedAnswer":83},"How was SmartHomeSecure evaluated, and what were the key results?",{"text":84,"@type":76},"The system was tested on 100 real-world Home Assistant YAML files with manually injected errors across five categories. Three evaluated models achieved 100% error detection accuracy, with repair success rates between 87% and 93%, and successful outputs showed no hallucinated or incorrect repairs upon manual verification.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,112,117,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":28,"slug":111},"technology",{"id":113,"doc_module":4,"doc_module_name":45,"category_name":114,"show_sort_weight":115,"slug":116},7,"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]