[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125969-en":3,"doc-seo-125969-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125969,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Naming the Pain in Machine Learning-Enabled Systems Engineering","Machine learning (ML)-enabled systems are increasingly adopted to improve products and operational processes, creating new engineering challenges across the software lifecycle. This paper surveys engineering practitioners internationally to capture current practices and recurring problems in ML-enabled systems engineering. The study includes 188 complete responses from 25 countries, using quantitative statistical analyses with bootstrapping and confidence intervals alongside qualitative coding of reported issues. Results map problems to each ML lifecycle phase, highlight factors behind project failure, and support adapting software engineering practices to strengthen practical ML systems.","Naming the Pain in Machine Learning-Enabled Systems Engineering  \nMarcos Kalinowskia, Daniel Mendez b, Grkem Girayc, Antonio Pedro Santos Alvesa, Kelly Azevedoa, Tatiana Escovedoa, Hugo Villamizara, Helio Lopesa , Teresa Baldassarred, Stefan Wagnere, Stefan Bifflf, Jrgen Musilf,  \nMichael Feldererg, h, Niklas Lavessonb, Tony Gorschekb  \naPontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil bBlekinge Institute of Technology (BTH), Sweden  \ncIndependent Researcher, Turkey  \ndUniversity of Bari, Italy  \ne Technical University of Munich (TUM), Germany  \nfVienna University of Technology (TUW), Austria gGerman Aerospace Center (DLR), Germany  \nh University of Cologne, Germany  \nAbstract  \nContext: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes.  \nObjective: This paper aims to deliver a comprehensive overview of the current status quo of engineering ML-enabled systems and lay the foundation to steer practically relevant and problem-driven academic research.  \nMethod: We conducted an international survey to collect insights from practitioners on the current practices and problems in engineering ML-enabled systems. We received 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems using open and axial coding procedures.  \nResults: Our survey results reinforce and extend existing empirical evidence on engineering ML-enabled systems, providing additional insights into typical ML-enabled systems project contexts, the perceived relevance and complexity of ML life cycle phases, and current practices related to problem understanding, model deployment, and model monitoring. Furthermore, the qualitative analysis provides a detailed map of the problems practitioners  \nface within each ML life cycle phase and the problems causing overall project failure.  \nConclusions: The results contribute to a better understanding of the status quo and problems in practical environments. We advocate for the further adaptation and dissemination of software engineering practices to enhance the engineering of ML-enabled systems.  \nKeywords: survey, machine learning-enabled system, systems engineering  \n1. Introduction  \nCompanies from different sectors are increasingly incorporating machine learning (ML) components into their software systems. We refer to these software systems, where an ML component is part of a larger system, as MLenabled systems. The shift from engineering conventional software systems to ML-enabled systems comes with challenges related to the idiosyncrasies of such systems, such as the high dependency on data, addressing additional qualities properties (e.g., fairness and explainability), dealing with iterative experimentation, and facing unrealistic assumptions [31, 25] . In fact, thenon-deterministic nature of ML-enabled systems poses software engineering (SE) challenges [7] and there are many specific concerns to be considered when engineering ML-enabled systems [32] .  \nMature tools and techniques for engineering ML-enabled systems are still missing [7], and SE can play an important role in resolving issues associated with the development of these systems. For instance, the literature suggests that requirements engineering (RE) can help to address problems related to dealing with customer expectations and to better aligning requirements with data [33, 31, 1] . Furthermore, designing and developing ML-enabled systems is complex and can be eased by having a good software architecture and making effective design decisions [26] . Indeed, many challenges of ML-enabled systems can be addressed from a software architecture perspective [19, 25, 26], and software architecture is also a cornerstone for effective production deployment of such systems [37] .  \nUnderstanding the status quo i","cbCailEMr9EcqUeR","https://ap.wps.com/l/cbCailEMr9EcqUeR","pdf",776972,6,1,39,"English","en",105,"# Introduction\n## Background and challenges of ML-enabled systems\n## Survey approach and research motivation\n## Overview of reported findings\n# Abstract\n# Keywords\n# Results and implications","[{\"question\":\"What is the main objective of the paper?\",\"answer\":\"To provide a comprehensive overview of the current state of engineering ML-enabled systems and establish a basis for practically relevant, problem-driven academic research.\"},{\"question\":\"How was the survey conducted?\",\"answer\":\"An international survey collected insights from practitioners, yielding 188 complete responses from 25 countries. Quantitative analysis used bootstrapping with confidence intervals, while qualitative analysis used open/axial coding.\"},{\"question\":\"What do the findings contribute for ML-enabled systems engineering?\",\"answer\":\"They reinforce and extend empirical evidence by characterizing typical project contexts, perceived relevance and complexity across ML lifecycle phases, and current practices for understanding problems, deploying models, and monitoring them.\"}]","Naming the Pain in Machine Learning-Enabled Systems Engineering | PDF",1785902292,98,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"naming-the-pain-in-machine-learning-enabled-systems-engineering","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/naming-the-pain-in-machine-learning-enabled-systems-engineering/125969/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main objective of the paper?","Question",{"text":77,"@type":78},"To provide a comprehensive overview of the current state of engineering ML-enabled systems and establish a basis for practically relevant, problem-driven academic research.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How was the survey conducted?",{"text":82,"@type":78},"An international survey collected insights from practitioners, yielding 188 complete responses from 25 countries. Quantitative analysis used bootstrapping with confidence intervals, while qualitative analysis used open/axial coding.",{"name":84,"@type":75,"acceptedAnswer":85},"What do the findings contribute for ML-enabled systems engineering?",{"text":86,"@type":78},"They reinforce and extend empirical evidence by characterizing typical project contexts, perceived relevance and complexity across ML lifecycle phases, and current practices for understanding problems, deploying models, and monitoring them.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]