Insight最新文献

{"title":"Decomposing Complexity: A Systems Engineering View of Modern Software — Aligning the V-Model with a Slicing-Based Decomposition Strategy","authors":"Shreya Sridhar","doi":"10.1002/inst.70075","DOIUrl":"https://doi.org/10.1002/inst.70075","url":null,"abstract":"<div>\u0000 \u0000 <p>Modern software-intensive systems–particularly in regulated domains such as medical devices -have reached a level of complexity that challenges traditional approaches to system decomposition. While the V-model has long served as a foundational systems engineering framework for ensuring traceability and verification rigor, its practical application in large-scale software programs often reveals a disconnect between how systems are decomposed and how they are ultimately built, integrated, and verified.</p>\u0000 <p>A growing need has emerged for a decomposition approach that preserves the intent of the V-model while adapting to the realities of iterative development, cross-functional dependencies, and continuous integration. One such approach is slicing, a systems-oriented decomposition strategy that reframes how complexity is structured shifting from component-centric thinking to behavior-driven, end-to-end system realization.</p>\u0000 <p>This article explores the parallel between the V-model and slicing, arguing that slicing does not replace the V-model, but rather operationalizes it in a way that is more aligned with modern software development.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 4","pages":"34-37"},"PeriodicalIF":0.8,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of the SERC AI4SE & SE4AI Workshop Series, and a Lookahead","authors":"Megan Clifford","doi":"10.1002/inst.70064","DOIUrl":"https://doi.org/10.1002/inst.70064","url":null,"abstract":"<div>\u0000 \u0000 <p>This technical review synthesizes the evolution of the AI4SE &amp; SE4AI workshop series from 2020 through 2025, with emphasis on the event's technical maturation, recurring themes, emerging patterns, and future relevance. The review considers the workshop as both an event series and a signal of disciplinary development: it reflects how the AI and systems engineering communities are defining the engineering practices required to transform AI from an emerging capability into an assured, operationally credible component of complex systems.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"15-18"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who Gets to Decide? An Organizational Decision-Making Lens for AI Governance","authors":"Jimmie G. McEver","doi":"10.1002/inst.70060","DOIUrl":"https://doi.org/10.1002/inst.70060","url":null,"abstract":"","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"95-97"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FROM THE EDITOR-IN-CHIEF","authors":"William Miller","doi":"10.1002/inst.70051","DOIUrl":"https://doi.org/10.1002/inst.70051","url":null,"abstract":"","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Curation and Execution of Engineering Models","authors":"Jaya Kambhampaty,&nbsp;Olivia Fischer,&nbsp;Dimitri Mavris","doi":"10.1002/inst.70054","DOIUrl":"https://doi.org/10.1002/inst.70054","url":null,"abstract":"<div>\u0000 \u0000 <p>The 2020s have been marked by increased attention to data-intensive information systems that maintain consistency with the evolving state of physical assets. We call the digital models of the connected physical systems “digital twins” (AIAA Digital Engineering Integration Committee 2021), while “digital thread” refers to the foundational framework of protocols, standards, and linked representations that maintains contextual meaning and traceability as data crosses tool, discipline, organizational, and lifecycle boundaries (AIAA Digital Engineering Integration Committee 2023). The practical and theoretical methods for producing these systems make up digital engineering (Griffin 2018), a discipline that formalizes and institutionalizes modeling as a primary, lifecycle-spanning engineering approach and is characterized by improved data sharing, model interoperability, and effective reuse.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"32-36"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering Trust in AI-Enabled Systems: Lessons from the Trusted AI Challenge","authors":"Sami Saliba,&nbsp;Emma Meno,&nbsp;Scott Lucero,&nbsp;Tyler Cody,&nbsp;Nathan Lau,&nbsp;Carlo Lipizzi,&nbsp;Hunter Moore,&nbsp;Jitesh Panchal,&nbsp;Sachin Shetty,&nbsp;Zoe Szajnfarber,&nbsp;Peter Beling","doi":"10.1002/inst.70057","DOIUrl":"https://doi.org/10.1002/inst.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>This article contributes a systems engineering perspective on trust in AI-enabled systems by synthesizing results from the Trusted AI Challenge. The Systems Engineering Research Center (SERC), with the U.S. Army DEVCOM Armaments Center at Picatinny Arsenal New Jersey, organized and conducted this student challenge across three academic semesters with six student/faculty teams. The challenge highlighted a broader evolution in systems engineering—from managing deterministic systems to architecting adaptive, human-centered systems in which trust must be continuously engineered and sustained. The challenge demonstrated engineering trust in AI is not solely a computer science problem—it is fundamentally a systems engineering challenge. By requiring teams to balance safety, explainability, and performance in partially observable, adversarial environments, the challenge catalyzed the development of actionable systems engineering methods.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"67-74"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systems Architecture for Data-Constrained Environments: Leveraging the Astronaut Digital Twin to Improve Spaceflight Adaptation in Humans","authors":"Caleb Schmidt,&nbsp;Steve Simske","doi":"10.1002/inst.70066","DOIUrl":"https://doi.org/10.1002/inst.70066","url":null,"abstract":"<div>\u0000 \u0000 <p>Spaceflight exposes humans to a uniquely harsh operating environment, producing highly individualized physiological responses that existing monitoring and countermeasure paradigms have limited struggle to predict or control. This challenge is compounded by small sample sizes, limited observability, fragmented evidence, and shifting baselines, creating a discordant epistemic environment where conventional data-driven, mechanistic, and hybrid models' methods are not well-suited. Resolving this gap is becoming increasingly urgent: the expanding scope, duration, and population diversity of human spaceflight missions have outpaced the predictive and personalization capability of space medicine now. This article proposes the astronaut digital twin (ADT), a biodigital twin architecture designed to help address three coupled problem classes: inference of physiological state under partial observability, reconciliation of heterogeneous evidence within a causal framework, and individualized countermeasure control under uncertainty. The ADT integrates systems physiology models, digital populations, and individualized digital twins within a Bayesian, constraint-based architecture that continuously updates state estimates and generates uncertainty-bounded, mechanistically interpretable recommendations. By formalizing expert knowledge into a testable human–machine co-learning system, the ADT is intended to enable a transition in space medicine from reactive monitoring toward predictive, personalized decision support. It also serves as a structured hypothesis engine for countermeasure development across diverse physiological profiles. More broadly, the ADT is positioned to engage with open SE4AI questions centered around interpretable reasoning, predictable and diagnosable behavior, and model validity under changing deployment conditions. Although developed for astronaut health, the architecture has the potential to generalize to other high-consequence domains characterized by sparse data, population diversity, and non-stationary baselines, suggesting its potential as a reference framework for trustworthy AI systems in complex operational environments.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"83-89"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing Human-AI Architectural Robustness to Inherently Brittle AI","authors":"Aditya Singh,&nbsp;Zoe Szajnfarber","doi":"10.1002/inst.70058","DOIUrl":"https://doi.org/10.1002/inst.70058","url":null,"abstract":"<div>\u0000 \u0000 <p>Machine learning based systems have made impressive advances in recent years, but they are still brittle, often failing when encountering new and unexpected changes to the operating environment. Humans are often partnered with AI-enabled systems to negate this brittleness, but human operators may struggle with changes to the scale of the operating environment, as cognitive fatigue can decrease the effectiveness of human oversight. Through a simulation of a notional minefield traversal system, we show how humans and AI are integrated together affects system outcomes. We propose that evaluations of human-AI systems should consider different human-AI integration architectures and that AI-enabled systems should also deploy continuous monitoring to detect shifts in the operating environment which may cause AI system performance to faulter. Finally, system design should enable human operators to have sufficient situational awareness, which may require scaling up the number of human operators available.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"75-82"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Rules to Agentic Swarms: A Systems Engineering Journey Through the Evolution of AI","authors":"Paul Wach PhD,&nbsp;Alejandro Salado PhD,&nbsp;Brad Philipbar","doi":"10.1002/inst.70052","DOIUrl":"https://doi.org/10.1002/inst.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) for systems engineering has evolved through six paradigms in five decades: expert systems, machine learning, deep learning, large language models, agentic AI, and agentic swarms, with each paradigm shorter than the last. This position article traces the arc through four systems the authors constructed across the paradigms, Houston (rule-based requirements), TurboArch (large-language-model architecture decisions), GenGroves (agentic model-based systems engineering), and MACQ (multi-agent acquisition support), supplemented by published work from researchers across the Systems Engineering Research Center (SERC) community for the machine-learning and deep-learning paradigms. A central observation runs through this history: each new paradigm has become a durable substrate for the next. Ontology and machine learning persist into later paradigms, and the article's portfolio of sixteen AI-for-SE systems shows how agentic swarms now compose those layers across the engineering lifecycle. The article does not attempt a comprehensive survey of AI; rather, the article identifies a critical inflection point through the lens of the authors and the SERC community. A critical observation from this article is that when organizational adoption lag exceeds paradigm duration, organizations risk investing in yesterday's frontier while missing the durable investment in the layers themselves. The systems engineering community must compress its own adoption cycles or accept permanent strategic lag.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"19-28"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Models to Systems: The Evolution of T&E for Artificial Intelligence from 2020 to 2030","authors":"Tyler Cody,&nbsp;Peter Beling","doi":"10.1002/inst.70059","DOIUrl":"https://doi.org/10.1002/inst.70059","url":null,"abstract":"<div>\u0000 \u0000 <p>Test and evaluation (T&amp;E) for artificial intelligence is entering a new phase. In 2020, the central challenge was to treat machine learning models as system functions rather than as ordinary software components. By 2025, that view had expanded: AI-enabled systems had to be evaluated in operational, organizational, and lifecycle context, with attention to trust, adaptation, resilience, and human-AI teaming. Looking toward 2030, an even larger shift is emerging. AI is no longer only part of the delivered capability; it is increasingly part of the engineering machinery that produces requirements, code, tests, models, documentation, and assurance evidence. This article traces that progression from model-centric assurance to lifecycle-centered assurance and, ultimately, to T&amp;E for AI-generated systems. It argues that future assurance will depend not only on testing delivered systems, but also on evaluating the provenance, governance, and stability of the generative pipelines that create and modify them.</p>\u0000 </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":"29 3","pages":"90-94"},"PeriodicalIF":0.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}