Managing The Development Of Software Intensive Systems

Managing the Development of Software-Intensive Systems provides both an introduction to project management for beginner software and hardware developers as well as unique advanced materials for experienced users. This beneficial resource presents realistic case studies for planning and managing verification and validation for large software projects, complex software, and hardware systems, as well as inspection results and testing metrics to monitor project status. Industrial practitioners and students will learn ways to improve how they manage and develop their project management applications and techniques to establish large software applications and systems.

The book describes how to manage and successfully deliver large, complex, and expensive systems that can be composed of millions of line of software code, being developed by numerous groups throughout the globe, that interface with many hardware items being developed by geographically dispersed companies, where the system also includes people, policies, constraints, regulations, and a myriad of other factors. It focuses on how to seamlessly integrate systems, satisfy the customer’s requirements, and deliver within the budget and on time. The guide is essentially a “shopping list” of all the activities that could be conducted with tailoring guidelines to meet the needs of each project.

Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems. Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.

Software Quality Assurance in Large Scale and Complex Software-intensive Systems presents novel and high-quality research related approaches that relate the quality of software architecture to system requirements, system architecture and enterprise-architecture, or software testing. Modern software has become complex and adaptable due to the emergence of globalization and new software technologies, devices and networks. These changes challenge both traditional software quality assurance techniques and software engineers to ensure software quality when building today (and tomorrow’s) adaptive, context-sensitive, and highly diverse applications. This edited volume presents state of the art techniques, methodologies, tools, best practices and guidelines for software quality assurance and offers guidance for future software engineering research and practice. Each contributed chapter considers the practical application of the topic through case studies, experiments, empirical validation, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited, to: quality attributes of system/software architectures; aligning enterprise, system, and software architecture from the point of view of total quality; design decisions and their influence on the quality of system/software architecture; methods and processes for evaluating architecture quality; quality assessment of legacy systems and third party applications; lessons learned and empirical validation of theories and frameworks on architectural quality; empirical validation and testing for assessing architecture quality. Focused on quality assurance at all levels of software design and development Covers domain-specific software quality assurance issues e.g. for cloud, mobile, security, context-sensitive, mash-up and autonomic systems Explains likely trade-offs from design decisions in the context of complex software system engineering and quality assurance Includes practical case studies of software quality assurance for complex, adaptive and context-critical systems

Architectural design is a crucial first step in developing complex software intensive systems. Early design decisions establish the structures necessary for achieving broad systemic properties. However, today's organizations lack synergy between software their development processes and technological methodologies. Providing a thorough treatment of

The growing scale and complexity of software-intensive systems are introducing fundamental new challenges of uncertainty and scale that are particularly demanding for defense systems. To assist in meeting these challenges, the Department of Defense asked the NRC to assess the nature of U.S. national investment in software research. As part of this study, a workshop was held to examine uncertainty at scale in current and future software-intensive systems. This report presents a summary of the workshop discussions that centered on process, architecture, and the grand scale; DoD software challenges for future systems; agility at scale; quality and assurance with scale and uncertainty; and enterprise scale and beyond. The report also offers a summary of key themes emerging from the workshop: architectural challenges in large-scale systems; the need for software engineering capability; and open questions and research opportunities.

Many software projects fail because their leaders don't know how to estimate, schedule, or measure them accurately. Fortunately, proven tools and techniques exist for every facet of software estimation. Estimating Software-Intensive Systems brings them together in a real-world guidebook that will help software managers, engineers, and customers immediately improve their estimates–and drive continuing improvements over time. Dick Stutzke presents here a disciplined and repeatable process that can produce accurate and complete estimates for any project, product, or process, no matter how new or unusual. Stutzke doesn't just describe formal techniques: He offers simple, easy-to-use templates, spreadsheets, and tools you can start using today to identify and estimate product size, performance, and quality–as well as project cost, schedule, and risk reserves. Stutzke shows how to quickly "get your arms around" users' problems and requirements, the structure of a solution, and the process needed to deliver it. You'll learn how to choose the most appropriate estimating techniques and tools; collect accurate data, track progress, and update estimates; and recalibrate estimating models to improve estimation accuracy. Stutzke's techniques apply whether you're creating custom in-house business software, purchasing or customizing "off-the-shelf" technology, or constructing complex, one-of-a-kind military, industrial, or commercial systems. These techniques apply to small and large projects, and to all project life cycles–from agile to plan-driven. This book will help you plan, estimate, budget, schedule, purchase, design, build, test, deploy, operate, and maintain software-intensive systems. It explains how to size software, identify all cost components, calculate the associated costs, and set a competitive price. A separate section covers topics of interest for large projects: designing an appropriate work breakdown structure, collecting data from cost accounting systems, and using earned value measurement. You'll find updates and even more information on this book's companion web site, http://www.sw-estimation.com.

The concept of processes is at the heart of software and systems engineering. Software process models integrate software engineering methods and techniques and are the basis for managing large-scale software and IT projects. High product quality routinely results from high process quality. Software process management deals with getting and maintaining control over processes and their evolution. Becoming acquainted with existing software process models is not enough, though. It is important to understand how to select, define, manage, deploy, evaluate, and systematically evolve software process models so that they suitably address the problems, applications, and environments to which they are applied. Providing basic knowledge for these important tasks is the main goal of this textbook. Münch and his co-authors aim at providing knowledge that enables readers to develop useful process models that are suitable for their own purposes. They start with the basic concepts. Subsequently, existing representative process models are introduced, followed by a description of how to create individual models and the necessary means for doing so (i.e., notations and tools). Lastly, different possible usage scenarios for process management are highlighted (e.g. process improvement and software process simulation). Their book is aimed at students and researchers working on software project management, software quality assurance, and software measurement; and at practitioners who are interested in process definition and management for developing, maintaining, and operating software-intensive systems and services.