Общий классификатор системных проблем. Часть I: Анализ, синтез, валидация и верификация

Systems engineering is the branch of engineering concerned with the development of large and complex systems, where a system is understood to be an assembly or combination of interrelated elements or parts working together toward a common objective. Past experience has shown that formal systems engineering methodologies have not always been successfully applied to large and complex cybersecurity systems. These complex systems have become commonplace when applying cyberstrategies in cybersecurity operations. The ability to build, operate and maintain such systems is crucial to the effectiveness of cybersecurity operations. Most importantly, a cyberstrategy program must surround these systems on a global scale across multiple inter-related platforms. In this chapter, the authors demonstrate why a systems engineering approach is best suited for large and complex information systems used in cybersecurity, as well as the overall cyberstrategies that must also reside over these systems.

The study conducted a systematic review with a bibliometric analysis to examine the extent of utilization and effectiveness of model-based systems engineering (MBSE) and concurrent engineering (CE) in managing and optimizing system design factors in complex systems across various domains, including space, healthcare, as well as active and assisted living and smart environments. The study aims to explore how MBSE and CE can address the inherent challenges in complex system definition and development, particularly focusing on their impact on system design factors such as mission analysis, system architecture, cost, schedule, and risk contingencies, which are commonly considered critical across the entire system lifecycle. By utilizing the PICO framework, the review formulates research questions and systematically searches multiple databases to identify relevant studies. The systematic review highlights that MBSE is prominently used in approximately 88% of the analyzed articles. These integrations enhance the methodologies’ ability to manage complexity and improve efficiency across various stages of the system lifecycle. Specialized tools such as MagicDraw, Cameo Systems Modeler, and OPCAT play a crucial role in the technical implementation of MBSE and CE, providing detailed diagrams and models that represent system components with their interactions and behavior. The principal findings highlight how MBSE and CE support product systems engineering (PSE) in the early lifecycle stages of complex systems of interest. This support is particularly evident in optimizing system design, reducing time, costs, and technological risks, and enhancing the efficiency of business systems engineering through lifecycle management and quality management.

The increasing complexity of technical systems can only be managed by a multi-disciplinary and holistic approach. Besides technical disciplines like aerodynamics, kinematics, etc. cross-disciplines like safety and project management play an immanent role in the Systems Engineering approach. In this chapter, standards from different cross-disciplines are discussed and merged together to elaborate synergies which enable a more holistic Systems Engineering view. After this introductory section, definitions of the terms system and complexity are given and the problems associated with the development of complex systems are introduced. The third section presents existing development philosophies and procedures. Additionally the mentioned cross-disciplines are introduced together with international standards widely established in the respective fields. Because the selected standards are not only complementary but also overlapping, the fourth section describes the harmonization approach carried out, together with the resulting holistic view. This combination of the standards enhances the benefits of the “traditional” Systems Engineering approach and solves many of the mentioned problems associated to the development of complex systems by taking also project management and safety aspects into a deeper and therefore, more holistic, account.

Systems engineering is a transdisciplinary scientific and applied direction that combines the principles of a systems approach, methods, tools and standards that provide an integrated methodology and relevant technologies for the design and development of complex systems, their comprehensive analysis and verification, efficient and safe use. The modern approach to systems engineering is characterized by an accentuated modeling of systems throughout their life cycle with the ultimate goal of creating their digital twins. It is called model-based systems engineering (MBSE). Consistent integration of MBSE with software engineering leads to the formation of the MBSSE (Model-Based Systems and Software Engineering) direction. This textbook is devoted to this area, the main objective of which is to study the conceptual and scientific-methodological foundations of MBSE and MBSSE, models and methods of life cycle management of systems and software, basic standards of systems engineering, study and mastery of the UML and SysML systems modeling languages and the corresponding modeling tools, study of requirements engineering methods and development of a description of the system architecture, familiarization with the concept of digital twins and their role in MBSE, study of the MBSSE reference model and its relationship with the process standards of systems engineering, study of the methodological aspects of the system and software integration process. The final chapter of the textbook is devoted to the study of the mathematical foundations of systems engineering. The main aspects of the mathematical theory of systems by A. Wayne Wymore, which became an important stimulus for the development of the model-oriented approach in systems engineering, and modern research, including the apparatus of finite automata by David Harel, the formalism of modeling of discrete systems DEVS, research into category theory as a formal mathematical basis for model-based system design are considered. The authors recommend this course as a basic course for training IT professionals.

One of the few things certain about development of complex systems is the requirements for the system will remain uncertain late into the development program. Even though we recognize this, why do many programs assume that requirements will not change (gambling) or treat requirement change as a risk rather than a certainty? An analysis of gas turbine engine control systems requirements shows that typically 50% will change between Critical Design Review and Entry into Service. This requirements uncertainty manifests as technical risk to the programme. This paper evaluates the impact of not managing these uncertainties and describes how applying Systems Engineering principles can reduce this effect.Design iterations produce rework, and without technical risk management, ~50% of the effort will be wasted in producing unacceptable designs. In turn, correction of these errors will create more rework. This results in much iteration and cumulatively twice as much work is required to produce a mature design. With technical risk management, less than 10% of the design effort will result in rework, giving a mature product with far less iteration.Investigation into the source of the product development lifecycle problems indicates many escapes occur during requirements definition and review. Around twice as many changes are driven by errors in requirement definition by the developer, compared to customer‐driven requirements changes. In addition, many of these escapes are detected later in the product development lifecycle than they could have been found, resulting in further escalation of the cost of product development. This paper compares and contrasts the root causes for these escapes during system, software and hardware development and looks at the differences in consequences.The role of the Systems Engineer is critical in addressing these problems. In addition to effective elicitation of requirements from stakeholders, the Systems Engineer must manage volatility and apply robust design principles to protect against the impact of requirements changes. The Systems Engineer must flow requirements effectively from system to sub‐system, from sub‐systems to components and provide the “glue” that results in the integrated system being more than the sum of its parts.Some tools and techniques are presented to help Systems Engineers identify probable sources of uncertainty and provide effective mitigations. An approach is also presented for assessing the technical risk management “maturity” of a project, based upon ‘best practice’ approaches taken from those projects achieving low levels of engineering scrap and rework during their development phase.

The increasing dynamism and variety of relationships among systems and within systems require new approaches in order for them to be developed in a manageable fashion. Systems engineering offers a structured and connected way of working that focuses on holistic and interdisciplinary systems thinking and is used for the efficient and effective development of complex systems. The subject is introduced from a systems science point of view and is followed by basic definitions and the fundamental principles of systems engineering. Furthermore, technical development philosophies, activities, and considerations along the system lifecycle are discussed. Systems engineering is seen as a comprehensive approach for the development of socio-technical complex systems.

Systems engineers and software engineers work together in the development of modern complex systems. The two engineering cultures, the concepts, and the best practices have developed independently over four decades. Notations and naming conventions for the same things are often different. Yet the efficient exchange of engineering information and wisdom between the two professions is important to the successful development of large complex systems. The present record of success for complex computer intensive systems is that for every six systems put in operation two are cancelled; on the average, projects are 50% over schedule, and three quarters are failures that do not function as intended or are not used at all, (Gibbs 1994). Incomplete specifications, ambiguous specifications, and misunderstood specifications are a major contributor to these problems. Development of rigorous specifications that match user needs is critical. The need for synergism between systems engineering which develops specifications to meet user need and software engineering is particularly important because software portions of systems are increasingly complex and are often being coded in countries far from the country where the system is defined and utilized.

Systems engineers and software engineers work together in the development of modern complex systems. The two engineering cultures, their concepts and their best practices have developed independently over four decades. Notations and naming conventions for the same things are often different. Yet the efficient exchange of engineering information and wisdom between the two professions is important to the successful development of large complex systems. The present record of success for complex computer-intensive systems is that for every six systems put into operation, two are canceled; on the average, projects are 50% over schedule; and three-quarters are failures that do not function as intended or are not used at all! Incomplete specifications, ambiguous specifications and misunderstood specifications are a major contributor to these problems. The development of rigorous specifications that match user needs is critical. The need for synergism between systems engineering which develops specifications to meet user needs and software engineering is particularly important because the software portions of systems are increasingly complex and are often being coded in countries far from the country where the system is defined and utilized. >

Complex systems are challenging for engineers. In considering the challenges in addressing complex problems as well as designing and developing complex systems, the INCOSE Complex Systems Working Group (CSWG) Heuristics Focus Team, in conjunction with the INCOSE Heuristics Team, has considered a range of systems engineering heuristics that guide the engineering of complex systems. These heuristics provide some initial insight for understanding the engineering of complex systems. This work aims to identify, develop, analyze and curate these heuristics and their potential use in dealing with complexity and developing complex systems. This paper concludes that a range of beneficial heuristics have been identified that cover the breadth of complex problems, as assessed from multiple perspectives. This initial or preliminary set of heuristics needs to be tested through practice and use across the INCOSE community before effort is expended to make them more memorable, either individually, or as a set.

Facilitating data analytics for effective prediction in complex products or systems development is the focus of the research described in this paper. The specific objective was to develop strategies and a data analytics pipeline with a view to supporting exploration of the design space of complex products or systems upfront. The underlying challenges tackled included how to acquire and store raw data gathered by using both the traditional methods and advanced Internet of Things (IoT) devices, how to preprocess and transform raw data into a form suited for data analytics, and how to deal with analytics. A pipeline for data analytics to support decision making in complex products or systems development is proposed and its applicability illustrated with a practical example. The incorporation of advanced analytics techniques into the proposed pipeline allows users to acquire data and to insightfully and intelligently predict aspects such as cost and assembly time early on, and to make decisions based on data that may otherwise deemed to be inaccessible or unusable. This work contributes to the efforts directed toward applying data analytics techniques in a way that can have a profound impact on an engineering product or system development process.

EXTENDED ABSTRACT As holistic thinking goes, the effects of the system on the human are well-known in the HFES community. However, many practitioners have experienced the challenges of incorporating these effects into a life cycle approach. Systems engineering seeks to model, predict, and employ holistic thinking in the development of multi-part and complex systems (NASA Systems Engineering Handbook). It is posited that a systemic approach and integration of human factors (HF) would better streamline various process pieces, thus reducing life cycle costs and risks to system and human functionality in the deployed system. Systems engineering processes and human factors philosophies have run parallel and intersecting courses, but rarely are they well-integrated. Despite the demonstrated impact of consideration on operators and users early in the design process for the reduction of system life cycle costs, and on increased adoption of systems by those users – few resources and tools exist that allow practitioners to translate human factors principles into Systems Engineering. These same practitioners are often called upon as experts within a systems development process. Limited participation on integrated product teams (IPTs), constrained and niche issue studies, and late life cycle validation all contribute to the challenges of holistic system integration, eventually passing frustration on to the users. The recent introduction of Human Readiness Levels (HRLs), which provide corresponding metrics to the well-established Technology Readiness Levels (TRLs), is one critical tool that will be faciliatory for appropriate consideration of human capabilities and limitations as part of system design (ANSI- HFES 400-2021). Tools like these that can become standard practices will be invaluable for inclusion of the human element into programs of record. However, in many instances, the practical considerations of conducting engineering design are often left to the individual practitioner to figure out. While many academic resources highlight the importance of including HF into system design and development, few resources exist to support in-the-trenches development efforts. In addition, communication of the value of human systems engineering across disciplines is consistently and persistently a challenge and incorporated as part of project contracts and plans to only a limited extent. This panel explores the issue, tales from the jungle, and success stories from multiple points of view, including practitioners of Human Factors, Systems Engineers, and Academics.

Gazprom Neft Science and Technology Center tailors various system engineering methods and other practices to the agenda of oil and gas industry. Resulting consistent approaches will produce a sort of work book enabling management of complex projects throughout the Upstream perimeter. Value-Driven Engineering is a strategic approach to system engineering that optimizes several disciplines within a single model. For example, complex project components are broken down into simpler elements, making it easier to find responsible action officers. Planning is broken down into phases that make it easier to meet the assigned deadlines. It allows you to fragmentize the end product at the design and management phase with a view to edit the product's configuration during the work. Essentially, the VDE approach best resembles a step-by-step guide to putting together a construction made up of multiple elements: without this guide, building the elements into one piece is a much harder job. System engineering is being successfully employed by NASA and aircraft industry today. The approach helps bring together numerous correlated technologies in spacecraft and aircraft building. In the oil industry, BP and Shell are the pioneers in using VDE. Seeking to tailor the system engineering approaches to the applied problems of Gazprom Neft, the Company engineers deliver work in several stages. Stage one is a look back study of projects that covers all the aspects of oil production, from seismic survey to field operation. To build the optimal concept, a project team studies special literature and existing practices in related sectors, essentially among foreign counterparts. The Company has already analyzed the existing research breakthroughs, best practices and digital tools. Even though VDE will chiefly focus on the development of new reservoirs, its individual practices may be successfully utilized at existing assets. Oil and gas production system is growing more complex every day because of the number of control elements and uncertainties that the oil and gas Company has to face at the early stages of planning a future asset. Development of each product, from concept to final implementation, involves a number of lifecycle stages; the sequence of these stages and the necessary toolkit for each stage is identified by the area of expertise known as system engineering. System engineering works perfectly if a certain product or system has existing equivalents, but engineers today may have to handle their tasks in absence of equivalent solutions, which necessitates engagement of creative competences. Development of such competences and inventive problem solving are in the focus of the area of expertise known as creative problem solving that relies on the TRIZ methods (TRIZ = theory of inventive problem solving). Technology intelligence is the area of expertise that focuses on aggregation of experience and employment of solutions from related industries or even from fundamental science. It allows engineering teams to work in an orderly and consistent fashion to find appropriate solutions in nature or in other areas of expertise and to accumulate such solutions in the Company's knowledge cloud. Development of complex systems and products, which include reservoir management, requires multidisciplinary engineering teams. An area of expertise known as team leadership is designed to make collaboration among team members more efficient. Value-Driven Engineering (VDE) is premised on the fundamental principles of systematic thinking of an engineer and human creativity. The conceptual framework of Value-Driven Engineering is shown in Figure 1. Figure 1 Conceptual framework of Value-Driven Engineering The concept involves four key areas of expertise: System engineering, i.e. the set of practices to control the technological system/product development process; Inventive problem solving, i.e. the methods and tools used to catalyze creative competence and problem solving skills; Technology intelligence, i.e. management of comprehensive scouting for human resources and new technologies; Team leadership, i.e. step-by-step guide to transform a group of specialists into a successful team by means of identifying the optimal team size and balance of roles and building a leadership system (goal, mission). This article provides a detailed outlook on the above methods and practices of tackling the challenges faced by the oil and gas industry.

Due to an expanding number of mechatronic functionalities in modern technical products, the proportion of software and electronic components is also increasing. As a result, the products are developed by different engineering domains in complex development processes. To handle the growing complexity, Systems Engineering (SE) is increasingly important for development organizations of enterprises. System Engineering (SE) is understood as an approach to network the individual engineering domains and shall lead to a collaborative development of complex systems. Model-Based System Engineering (MBSE) expands SE by using common models and software tools to describe und visualize the systems. However, MBSE is not widely established in enterprises today. On the one hand, the introduction requires a distinct and consistent system understanding and collaborative way of working. On the other hand, the application of the existing tools requires extensive tool competencies due to many possible functions and features. Therefore, this paper presents a concept and a software based tool for a lean implementation of SE/MBSE to support the collaborative development of complex technical systems in small and medium-sized enterprieses.

Software models play a significant role with the growth of software system development based on Model Driven Development (MDD) approach. Model transformations and compositions are the heart of MDD and allow the development of complex systems and their automated derivation. Moreover, software development of large and complex systems uses a collection of models, where model composition and decomposition are required. Various research studies have been done on specifying and executing MDD processes; however only a few of those have considered the validity of such transformations, thus safe composition and decomposition of models. This paper presents a general approach for model composition for the transformation from UML sequence diagrams to Coloured Petri Nets and validates the correctness of model composition using a mathematical proof. These transformations are based on formal rules, which have already been proven to be strongly consistent.

: As current United States Department of Defense (DoD) system development and engineering activities continue to be challenged by formulation of larger and more complex systems, DoD's methods, processes, and tools (MPT) for effectively and efficiently addressing these challenges are likewise being challenged. The goal of this research was to develop a mixed methodological approach to examine systems development maturity. Qualitatively we intended to uncover and investigate the key characteristics that drive the development of large scale, complex systems. Quantitatively we used these key characteristics to formulate a collection of analytical MPT to assist in making informed systems engineering management decisions. To advance the state of practice of this research, all MPT developed under this task were validated through application on designated projects. The validation effort was designed to determine if they could be effectively implemented as a best practice across the Department of Defense. The need for this research is precipitated by the need for system engineering, development, and cost models that adequately incorporate the unique aspects of system and technology insertion and integration. This was then predicated on the following task objectives: *Leverage prior investments made in the System Readiness Level (SRL) body of knowledge to explore the effects of technology and integration maturity on systems engineering effort and cost *Expand the scope and applicability of the SRL to address potential systems engineering MPT; and *Enhance current SRL methods and tools to incorporate research-derived insights, provide expanded functionality, and demonstrate the utility of the tools in the context of a pilot project. At present, the SRL is a descriptive model that characterizes the effects of technology and integration maturity on a system engineering effort, particularly with respect to integrating discrete functional systems into a coherent mission capability.