[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-657467-105":59,"doc-detail-657467-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","no-code-ai-automatic-generation-of-function-block-diagrams-from-documentation-and-associated-heuristic-for-context-aware-ml-algorithm-training","No Code AI - Automatic generation of Function Block Diagrams from documentation and associated heuristic for context-aware ML algorithm training","","Industrial process engineering and PLC program development commonly rely on Function Block Diagram (FBD) programming rather than imperative approaches. Growing adoption of “No Code” and “Low Code” concepts, alongside machine learning momentum, is changing how executable programs are structured from digital requirements. The work proposes generating executable FBD programs from requirement documents using a constrained selection algorithm informed by a recommender-system-style utility. Results indicate this generative no-code approach is viable for industrial process design.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/no-code-ai-automatic-generation-of-function-block-diagrams-from-documentation-and-associated-heuristic-for-context-aware-ml-algorithm-training/657467/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/no-code-ai-automatic-generation-of-function-block-diagrams-from-documentation-and-associated-heuristic-for-context-aware-ml-algorithm-training/657467.png","ImageObject",300,407,{"name":92,"@type":93},"miaandmiso","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-26",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the proposed method generate executable FBD programs from documentation?","Question",{"text":112,"@type":113},"It derives either intermediate or final FBD artifacts from requirement documents using a constrained selection algorithm. The algorithm selects function blocks based on conditional probabilities computed from an associated utility function that can act like a recommender system.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why are heuristic methods insufficient for larger industrial control systems?",{"text":117,"@type":113},"Heuristic translation can work for small systems, but as system size grows it becomes insufficient and more error-prone. The paper contrasts this with formal methods that use well-designed algorithms to reduce effort and time.",{"name":119,"@type":110,"acceptedAnswer":120},"What role do formal methods and learning approaches play in this work?",{"text":121,"@type":113},"Formal methods can automatically generate control code but require strong mathematical knowledge from practitioners. Learning-based approaches are introduced to reduce modeling complexity by translating informal requirements into formal specifications, and the paper explores combining these ideas for executable FBD generation.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},657467,1793124887,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},7971478544854,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","No Code AI: Automatic generation of Function Block Diagrams from documentation and associated heuristic for context-aware ML algorithm training  \nOluwatosin Ogundare, Gustavo Quiros Araya, Yassine Qamsane  \nSiemens Technology  \nPrinceton, NJ, USA  \n[gustavo.quiros@siemens.com](gustavo.quiros@siemens.com)  \nAbstract—Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as “No Code” or “Low Code” alongside the mainstream success of statistical learning theory or the so-called machine learning is redefining the way in which we structure programs for the digital machine to execute. A principal focus of “No Code” is deriving executable programs directly from a set of requirement documents or any other documentation that defines consumer or customer expectation. We present a method for generating Function Block Diagram (FBD) programs as either the intermediate or final artifact that can be executed by a target system from a set of requirement documents using a constrained selection algorithm that draws from the top line of an associated recommender system. The results presented demonstrate that this type of No-codegenerative model is a viable option for industrial process design.  \nKeywords- No Code; ML training heuristic; Statistical learning; AI for Industrial Engineering  \nI. INTRODUCTION  \nIn industrial control, Function Block Diagrams (FBD) are widely used for designing control systems software implemented in Programmable Logic Controllers (PLC) . FBD programming is one of the standard PLC programming languages defined in the IEC 61131-3 standard [1] and its extension, the IEC 61499 standard [2] . Currently, there is a fast-growing number of methods and heuristics for code generation from requirements documentation. Many of these solutions rely on the simplicity of FBDs to support the idea that code generation from documentation is industrially viable. Even in cases where scaffolded object oriented, or functional code are generated as the final artifact, FBDs are sometimes generated as the intermediate or secondary artifact.  \nCode generation in current industrial control practice is typically based on either heuristic methods or formal methods. Heuristic methods intuitively translate the requirements from documentation into control code based on the practitioner’s ingenuity [3][4] . For small size systems, this direct interpretation of the requirements into control code may yield practical solutions, however, as the system size becomes larger, the method becomes insufficient and error prone. To assist the control practitioners in their task, formal methods can be exploited to automatically generate control code.  \nA specification language for control code generation based on linear temporal logic is widely adopted in the industry [5] . Event-B formalism is also widely used to design PLC control code and requires formal proofs that the designed code satisfies completeness, consistency, precision, and correctness throughout the modeling and refinement process [6] . Other methods combine several formal languages with PLC standard languages, such as automata and GRAFCET/SFC [7], Petri nets and Ladder diagrams [8][9], and Max Plus Algebra with Petri Nets [10][11] .  \nFormal methods outperform conventional heuristic methods as they use well designed algorithms to automatically generate control code with less effort and time. However, from a practical standpoint, formal methods require the control practitioner to have strong knowledge of advanced mathematical concepts used to either model the requirements from documents orthe system/process to control or both. Most control practitioners in industry do not have the necessary knowledge to work with formal methods. Thus, there is a need ","cbCaifmYTj4BhBGh","https://ap.wps.com/l/cbCaifmYTj4BhBGh","pdf",383591,"English","# Introduction\n## Code generation from requirements: heuristic vs formal methods\n## Formal specification and model-based approaches\n## Learning-assisted translation toward executable FBD programs\n# Axiomatic definition of context-aware industrial process design using FBD programming","[{\"question\":\"How does the proposed method generate executable FBD programs from documentation?\",\"answer\":\"It derives either intermediate or final FBD artifacts from requirement documents using a constrained selection algorithm. The algorithm selects function blocks based on conditional probabilities computed from an associated utility function that can act like a recommender system.\"},{\"question\":\"Why are heuristic methods insufficient for larger industrial control systems?\",\"answer\":\"Heuristic translation can work for small systems, but as system size grows it becomes insufficient and more error-prone. The paper contrasts this with formal methods that use well-designed algorithms to reduce effort and time.\"},{\"question\":\"What role do formal methods and learning approaches play in this work?\",\"answer\":\"Formal methods can automatically generate control code but require strong mathematical knowledge from practitioners. Learning-based approaches are introduced to reduce modeling complexity by translating informal requirements into formal specifications, and the paper explores combining these ideas for executable FBD generation.\"}]","No Code AI - Automatic generation of Function Block Diagrams from documentation and associated heuristic for context-aware ML algorithm training | PDF",1792980391,15]