Systems' Verification Validation and Testing (VVT) are carried out throughout systems' lifetimes. Notably, quality-cost expended on performing VVT activities and correcting system defects consumes about half of the overall engineering cost. Verification, Validation and Testing of Engineered Systems provides a comprehensive compendium of VVT activities and corresponding VVT methods for implementation throughout the entire lifecycle of an engineered system. In addition, the book strives to alleviate the fundamental testing conundrum, namely: What should be tested? How should one test? When should one test? And, when should one stop testing? In other words, how should one select a VVT strategy and how it be optimized? The book is organized in three parts: The first part provides introductory material about systems and VVT concepts. This part presents a comprehensive explanation of the role of VVT in the process of engineered systems (Chapter-1). The second part describes 40 systems' development VVT activities (Chapter-2) and 27 systems' post-development activities (Chapter-3). Corresponding to these activities, this part also describes 17 non-testing systems' VVT methods (Chapter-4) and 33 testing systems' methods (Chapter-5). The third part of the book describes ways to model systems' quality cost, time and risk (Chapter-6), as well as ways to acquire quality data and optimize the VVT strategy in the face of funding, time and other resource limitations as well as different business objectives (Chapter-7). Finally, this part describes the methodology used to validate the quality model along with a case study describing a system's quality improvements (Chapter-8). Fundamentally, this book is written with two categories of audience in mind. The first category is composed of VVT practitioners, including Systems, Test, Production and Maintenance engineers as well as first and second line managers. The second category is composed of students and faculties of Systems, Electrical, Aerospace, Mechanical and Industrial Engineering schools. This book may be fully covered in two to three graduate level semesters; although parts of the book may be covered in one semester. University instructors will most likely use the book to provide engineering students with knowledge about VVT, as well as to give students an introduction to formal modeling and optimization of VVT strategy.

Comprehensive and up-to-date, it covers the most vital part of software development, independent verification and validation. Presents a variety of methods that will ensure better quality, performance, cost and reliability of technical products and systems. Features numerous hints, tips and instructions for better interaction between verification and validation personnel, development engineers and managers. Includes 8 case histories ranging from major engineering systems through information systems. Many of the principles involved also apply to computer hardware as well as the fields of science and engineering.

HereOCOs the first book written specifically to help medical device and software engineers, QA and compliance professionals, and corporate business managers better understand and implement critical verification and validation processes for medical device software.Offering you a much broader, higher-level picture than other books in this field, this book helps you think critically about software validation -- to build confidence in your softwareOCOs safety and effectiveness. The book presents validation activities for each phase of the development lifecycle and shows: why these activities are important and add value; how to undertake them; and what outputs need to be created to document the validation process.From software embedded within medical devices, to software that performs as a medical device itself, this comprehensive book explains how properly handled validation throughout the development lifecycle can help bring medical devices to completion sooner, at higher quality, in compliance with regulations."

This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.

The book outlines selected projects conducted under the supervision of the author. Moreover, it discusses significant relations between Interactive Granular Computing (IGrC) and numerous dynamically developing scientific domains worldwide, along with features characteristic of the author’s approach to IGrC. The results presented are a continuation and elaboration of various aspects of Wisdom Technology, initiated and developed in cooperation with Professor Andrzej Skowron. Based on the empirical findings from these projects, the author explores the following areas: (a) understanding the causes of the theory and practice gap problem (TPGP) in complex systems engineering (CSE); (b) generalizing computing models of complex adaptive systems (CAS) (in particular, natural computing models) by constructing an interactive granular computing (IGrC) model of networks of interrelated interacting complex granules (c-granules), belonging to a single agent and/or to a group of agents; (c) developing methodologies based on the IGrC model to minimize the negative consequences of the TPGP. The book introduces approaches to the above issues, using the proposed IGrC model. In particular, the IGrC model refers to the key mechanisms used to control the processes related to the implementation of CSE projects. One of the main aims was to develop a mechanism of IGrC control over computations that model a project’s implementation processes to maximize the chances of its success, while at the same time minimizing the emerging risks. In this regard, the IGrC control is usually performed by means of properly selected and enforced (among project participants) project principles. These principles constitute examples of c-granules, expressed by complex vague concepts (represented by c-granules too). The c-granules evolve with time (in particular, the meaning of the concepts is also subject of change). This methodology is illustrated using project principles applied by the author during the implementation of the POLTAX, AlgoTradix, Merix, and Excavio projects outlined in the book.

This volume comprises papers from the 18th Conference on Systems Engineering Research (CSER). The theme of this volume, “Recent Trends and Advances in Model-Based Systems Engineering,” reflects the fact that systems engineering is undergoing a transformation motivated by mission and system complexity and enabled by technological advances such as model-based systems engineering, digital engineering, and the convergence of systems engineering with other disciplines. This conference is focused on exploring recent trends and advances in model-based systems engineering (MBSE) and the synergy of MBSE with simulation technology and digital engineering. Contributors have submitted papers on MBSE methods, modeling approaches, integration of digital engineering with MBSE, standards, modeling languages, ontologies and metamodels, and economics analysis of MBSE to respond to the challenges posed by 21st century systems. What distinguishes this volume are the latest advances in MBSE research, the convergence of MBSE with digital engineering, and recent advances in applied research in MBSE, including growing convergence with systems science and decision science. This volume is appropriate as a reference text in graduate engineering courses in Model-Based Systems Engineering.

This reference resource serves as a tool to facilitate development engineers to develop new testing methodologies appropriate for new technological products as they evolve.

This volume chronicles the 16th Annual Conference on System Engineering Research (CSER) held on May 8-9, 2018 at the University of Virginia, Charlottesville, Virginia, USA. The CSER offers researchers in academia, industry, and government a common forum to present, discuss, and influence systems engineering research. It provides access to forward‐looking research from across the globe, by renowned academicians as well as perspectives from senior industry and government representatives. Co‐founded by the University of Southern California and Stevens Institute of Technology in 2003, CSER has become the preeminent event for researchers in systems engineering across the globe. Topics include though are not limited to the following: Systems in context: · Formative methods: requirements · Integration, deployment, assurance · Human Factors · Safety and Security Decisions/ Control & Design; Systems Modeling: · Optimization, Multiple Objectives, Synthesis · Risk and resiliency · Collaborative autonomy · Coordination and distributed decision-making Prediction: · Prescriptive modeling; state estimation · Stochastic approximation, stochastic optimization and control Integrative Data engineering: · Sensor Management · Design of Experiments