Reliability Analysis of Complex NASA Systems with Model-Based Engineering

The emergence of model-based engineering, with Model- Based Systems Engineering (MBSE) leading the way, is transforming design and analysis methodologies. The recognized benefits to systems development include moving from document-centric information systems and document-centric project communication to a model-centric environment in which control of design changes in the life cycles is facilitated. In addition, a single source of truth about the system, that is up-to-date in all respects of the design, becomes the authoritative source of data and information about the system. This promotes consistency and efficiency in regard to integration of the system elements as the design emerges and thereby may further optimize the design. Therefore Reliability Engineers (REs) supporting NASA missions must be integrated into model-based engineering to ensure the outputs of their analyses are relevant and value-needed to the design, development, and operational processes for failure risks assessment and communication

The Digital Transformation of Automotive Businesses: THREE ARTEFACTS TO SUPPORT DIGITAL SERVICE PROVISION AND INNOVATION

Digitalisation and increasing competitive pressure drive original equipment manufacturers (OEMs) to switch their focus towards the provision of digital services and open-up towards increased collaboration and customer integration. This shift implies a significant transformational change from product to product-service providers, where OEMs realign themselves within strategic, business and procedural dimensions. Thus, OEMs must manage digital transformation (DT) processes in order to stay competitive and remain adaptable to changing customer demands. However, OEMs aspiring to become participants or leaders in their domain, struggle to initiate activities as there is a lack of applicable instruments that can guide and support them during this process. Compared to the practical importance of DT, empirical studies are not comprehensive. This study proposes three artefacts, validated within case companies that intend to support automotive OEMs in digital service provisioning. Artefact one, a layered conceptual model for a digital automotive ecosystem, was developed by means of 26 expert interviews. It can serve as a useful instrument for decision makers to strategically plan and outline digital ecosystems. Artefact two is a conceptual reference framework for automotive service systems. The artefact was developed based on an extensive literature review, and the mapping of the business model canvas to the service system domain. The artefact intends to assist OEMs in the efficient conception of digital services under consideration of relevant stakeholders and the necessary infrastructures. Finally, artefact three proposes a methodology by which to transform software readiness assessment processes to fit into the agile software development approach with consideration of the existing operational infrastructure. Overall, the findings contribute to the empirical body of knowledge about the digital transformation of manufacturing industries. The results suggest value creation for digital automotive services occurs in networks among interdependent stakeholders in which customers play an integral role during the services’ life-cycle. The findings further indicate the artefacts as being useful instruments, however, success is dependent on the integration and collaboration of all contributing departments.:Table of Contents Bibliographic Description II Acknowledgment III Table of Contents IV List of Figures VI List of Tables VII List of Abbreviations VIII 1 Introduction 1 1.1 Motivation and Problem Statement 1 1.2 Objective and Research Questions 6 1.3 Research Methodology 7 1.4 Contributions 10 1.5 Outline 12 2 Background 13 2.1 From Interdependent Value Creation to Digital Ecosystems 13 2.1.1 Digitalisation Drives Collaboration 13 2.1.2 Pursuing an Ecosystem Strategy 13 2.1.3 Research Gaps and Strategy Formulation Obstacles 20 2.2 From Products to Product-Service Solutions 22 2.2.1 Digital Service Fulfilment Requires Co-Creational Networks 22 2.2.2 Enhancing Business Models with Digital Services 28 2.2.3 Research Gaps and Service Conception Obstacles 30 2.3 From Linear Development to Continuous Innovation 32 2.3.1 Digital Innovation Demands Digital Transformation 32 2.3.2 Assessing Digital Products 36 2.3.3 Research Gaps and Implementation Obstacles 38 3 Artefact 1: Digital Automotive Ecosystems 41 3.1 Meta Data 41 3.2 Summary 42 3.3 Designing a Layered Conceptual Model of a Digital Ecosystem 45 4 Artefact 2: Conceptual Reference Framework 79 4.1 Meta Data 79 4.2 Summary 80 4.3 On the Move Towards Customer-Centric Automotive Business Models 83 5 Artefact 3: Agile Software Readiness Assessment Procedures 121 5.1 Meta Data 121 5.2 Meta Data 122 5.3 Summary 123 5.4 Adding Agility to Software Readiness Assessment Procedures 126 5.5 Continuous Software Readiness Assessments for Agile Development 147 6 Conclusion and Future Work 158 6.1 Contributions 158 6.1.1 Strategic Dimension: Artefact 1 158 6.1.2 Business Dimension: Artefact 2 159 6.1.3 Process Dimension: Artefact 3 161 6.1.4 Synthesis of Contributions 163 6.2 Implications 167 6.2.1 Scientific Implications 167 6.2.2 Managerial Implications 168 6.2.3 Intelligent Parking Service Example (ParkSpotHelp) 171 6.3 Concluding Remarks 174 6.3.1 Threats to Validity 174 6.3.2 Outlook and Future Research Recommendations 174 Appendix VII Bibliography XX Wissenschaftlicher Werdegang XXXVII Selbständigkeitserklärung XXXVII

Model Based Mission Assurance in a Model Based Systems Engineering (MBSE) Framework: State-of-the-Art Assessment

This report explores the current state of the art of Safety and Mission Assurance (S&MA) in projects that have shifted towards Model Based Systems Engineering (MBSE). Its goal is to provide insight into how NASA's Office of Safety and Mission Assurance (OSMA) should respond to this shift. In MBSE, systems engineering information is organized and represented in models: rigorous computer-based representations, which collectively make many activities easier to perform, less error prone, and scalable. S&MA practices must shift accordingly. The "Objective Structure Hierarchies" recently developed by OSMA provide the framework for understanding this shift. Although the objectives themselves will remain constant, S&MA practices (activities, processes, tools) to achieve them are subject to change. This report presents insights derived from literature studies and interviews. The literature studies gleaned assurance implications from reports of space-related applications of MBSE. The interviews with knowledgeable S&MA and MBSE personnel discovered concerns and ideas for how assurance may adapt. Preliminary findings and observations are presented on the state of practice of S&MA with respect to MBSE, how it is already changing, and how it is likely to change further. Finally, recommendations are provided on how to foster the evolution of S&MA to best fit with MBSE

Boundary Objects and their Use in Agile Systems Engineering

Agile methods are increasingly introduced in automotive companies in the attempt to become more efficient and flexible in the system development. The adoption of agile practices influences communication between stakeholders, but also makes companies rethink the management of artifacts and documentation like requirements, safety compliance documents, and architecture models. Practitioners aim to reduce irrelevant documentation, but face a lack of guidance to determine what artifacts are needed and how they should be managed. This paper presents artifacts, challenges, guidelines, and practices for the continuous management of systems engineering artifacts in automotive based on a theoretical and empirical understanding of the topic. In collaboration with 53 practitioners from six automotive companies, we conducted a design-science study involving interviews, a questionnaire, focus groups, and practical data analysis of a systems engineering tool. The guidelines suggest the distinction between artifacts that are shared among different actors in a company (boundary objects) and those that are used within a team (locally relevant artifacts). We propose an analysis approach to identify boundary objects and three practices to manage systems engineering artifacts in industry

Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years

Preserving the Quality of Architectural Tactics in Source Code

In any complex software system, strong interdependencies exist between requirements and software architecture. Requirements drive architectural choices while also being constrained by the existing architecture and by what is economically feasible. This makes it advisable to concurrently specify the requirements, to devise and compare alternative architectural design solutions, and ultimately to make a series of design decisions in order to satisfy each of the quality concerns. Unfortunately, anecdotal evidence has shown that architectural knowledge tends to be tacit in nature, stored in the heads of people, and lost over time. Therefore, developers often lack comprehensive knowledge of underlying architectural design decisions and inadvertently degrade the quality of the architecture while performing maintenance activities. In practice, this problem can be addressed through preserving the relationships between the requirements, architectural design decisions and their implementations in the source code, and then using this information to keep developers aware of critical architectural aspects of the code. This dissertation presents a novel approach that utilizes machine learning techniques to recover and preserve the relationships between architecturally significant requirements, architectural decisions and their realizations in the implemented code. Our approach for recovering architectural decisions includes the two primary stages of training and classification. In the first stage, the classifier is trained using code snippets of different architectural decisions collected from various software systems. During this phase, the classifier learns the terms that developers typically use to implement each architectural decision. These ``indicator terms\u27\u27 represent method names, variable names, comments, or the development APIs that developers inevitably use to implement various architectural decisions. A probabilistic weight is then computed for each potential indicator term with respect to each type of architectural decision. The weight estimates how strongly an indicator term represents a specific architectural tactics/decisions. For example, a term such as \emph{pulse} is highly representative of the heartbeat tactic but occurs infrequently in the authentication. After learning the indicator terms, the classifier can compute the likelihood that any given source file implements a specific architectural decision. The classifier was evaluated through several different experiments including classical cross-validation over code snippets of 50 open source projects and on the entire source code of a large scale software system. Results showed that classifier can reliably recognize a wide range of architectural decisions. The technique introduced in this dissertation is used to develop the Archie tool suite. Archie is a plug-in for Eclipse and is designed to detect wide range of architectural design decisions in the code and to protect them from potential degradation during maintenance activities. It has several features for performing change impact analysis of architectural concerns at both the code and design level and proactively keep developers informed of underlying architectural decisions during maintenance activities. Archie is at the stage of technology transfer at the US Department of Homeland Security where it is purely used to detect and monitor security choices. Furthermore, this outcome is integrated into the Department of Homeland Security\u27s Software Assurance Market Place (SWAMP) to advance research and development of secure software systems

Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research