Ivan Mistrik – författare

Agile software development approaches have had significant impact on industrial software development practices. Today, agile software development has penetrated to most IT companies across the globe, with an intention to increase quality, productivity, and profitability. Comprehensive knowledge is needed to understand the architectural challenges involved in adopting and using agile approaches and industrial practices to deal with the development of large, architecturally challenging systems in an agile way.

Agile Software Architecture focuses on gaps in the requirements of applying architecture-centric approaches and principles of agile software development and demystifies the agile architecture paradox. Readers will learn how agile and architectural cultures can co-exist and support each other according to the context. Moreover, this book will also provide useful leads for future research in architecture and agile to bridge such gaps by developing appropriate approaches that incorporate architecturally sound practices in agile methods.

Presents a consolidated view of the state-of-art and state-of-practice as well as the newest research findings Identifies gaps in the requirements of applying architecture-centric approaches and principles of agile software development and demystifies the agile architecture paradox Explains whether or not and how agile and architectural cultures can co-exist and support each other depending upon the context Provides useful leads for future research in both architecture and agile to bridge such gaps by developing appropriate approaches, which incorporate architecturally sound practices in agile methods

Economics-driven Software Architecture presents a guide for engineers and architects who need to understand the economic impact of architecture design decisions: the long term and strategic viability, cost-effectiveness, and sustainability of applications and systems. Economics-driven software development can increase quality, productivity, and profitability, but comprehensive knowledge is needed to understand the architectural challenges involved in dealing with the development of large, architecturally challenging systems in an economic way.

This book covers how to apply economic considerations during the software architecting activities of a project. Architecture-centric approaches to development and systematic evolution, where managing complexity, cost reduction, risk mitigation, evolvability, strategic planning and long-term value creation are among the major drivers for adopting such approaches. It assists the objective assessment of the lifetime costs and benefits of evolving systems, and the identification of legacy situations, where architecture or a component is indispensable but can no longer be evolved to meet changing needs at economic cost. Such consideration will form the scientific foundation for reasoning about the economics of nonfunctional requirements in the context of architectures and architecting.

Familiarizes readers with essential considerations in economic-informed and value-driven software design and analysis Introduces techniques for making value-based software architecting decisions Provides readers a better understanding of the methods of economics-driven architecting

System Quality and Software Architecture collects state-of-the-art knowledge on how to intertwine software quality requirements with software architecture and how quality attributes are exhibited by the architecture of the system. Contributions from leading researchers and industry evangelists detail the techniques required to achieve quality management in software architecting, and the best way to apply these techniques effectively in various application domains (especially in cloud, mobile and ultra-large-scale/internet-scale architecture) Taken together, these approaches show how to assess the value of total quality management in a software development process, with an emphasis on architecture.

The book explains how to improve system quality with focus on attributes such as usability, maintainability, flexibility, reliability, reusability, agility, interoperability, performance, and more. It discusses the importance of clear requirements, describes patterns and tradeoffs that can influence quality, and metrics for quality assessment and overall system analysis. The last section of the book leverages practical experience and evidence to look ahead at the challenges faced by organizations in capturing and realizing quality requirements, and explores the basis of future work in this area.

Explains how design decisions and method selection influence overall system quality, and lessons learned from theories and frameworks on architectural quality Shows how to align enterprise, system, and software architecture for total quality Includes case studies, experiments, empirical validation, and systematic comparisons with other approaches already in practice.

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

Managing Trade-Offs in Adaptable Software Architectures explores the latest research on adapting large complex systems to changing requirements. To be able to adapt a system, engineers must evaluate different quality attributes, including trade-offs to balance functional and quality requirements to maintain a well-functioning system throughout the lifetime of the system.

This comprehensive resource brings together research focusing on how to manage trade-offs and architect adaptive systems in different business contexts. It presents state-of-the-art techniques, methodologies, tools, best practices, and guidelines for developing adaptive systems, 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, how to architect a system for adaptability, software architecture for self-adaptive systems, understanding and balancing the trade-offs involved, architectural patterns for self-adaptive systems, how quality attributes are exhibited by the architecture of the system, how to connect the quality of a software architecture to system architecture or other system considerations, and more.

Explains software architectural processes and metrics supporting highly adaptive and complex engineering Covers validation, verification, security, and quality assurance in system design Discusses domain-specific software engineering issues for cloud-based, mobile, context-sensitive, cyber-physical, ultra-large-scale/internet-scale systems, mash-up, and autonomic systems Includes practical case studies of complex, adaptive, and context-critical systems

Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity.

The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors.

Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques Presents case studies involving enterprise, business, and government service deployment of big data applications Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

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.

This book addresses the challenges in the software engineering of variability-intensive systems. Variability-intensive systems can support different usage scenarios by accommodating different and unforeseen features and qualities. The book features academic and industrial contributions that discuss the challenges in developing, maintaining and evolving systems, cloud and mobile services for variability-intensive software systems and the scalability requirements they imply. The book explores software engineering approaches that can efficiently deal with variability-intensive systems as well as applications and use cases benefiting from variability-intensive systems.

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.

This book addresses the challenges in the software engineering of variability-intensive systems. Variability-intensive systems can support different usage scenarios by accommodating different and unforeseen features and qualities. The book features academic and industrial contributions that discuss the challenges in developing, maintaining and evolving systems, cloud and mobile services for variability-intensive software systems and the scalability requirements they imply. The book explores software engineering approaches that can efficiently deal with variability-intensive systems as well as applications and use cases benefiting from variability-intensive systems.

Thirty years ago, I first entered the dark realm of software engineering, through a prior interest in documentation. In those days, documentation pretty much meant functional specifications. The idea that stakeholders in a system (its implementers, its end-users, its maintainers, and so forth) might want something other than an alphabetic list of function definitions was just taking hold. There was an exciting (to me) vision of stakeholders accessing and contributing to explanations of how and why aspects of a system work as they do, tradeoff analysis of concomitant downsides, and perhaps even accounts of why other possible approaches were not followed. There were many challenges to overcome in achieving this vision. The most formidable is the belief that people do not like to create or use do- mentation. This negative image of documentation is (unfortunately) more than just the bias of a few incorrigible system developers. It is more like a deep truth about human information behavior, about how human beings construe and act towards information. Humans are, by default, active users of information; they want to try things out, and get things done. When documentation is interposed as a prerequisite between people and a desired activity, they try to skip through it, circumvent it, or undermine it. Desi- ing information to suit the needs and interests of its users is an abiding challenge, but we have come a long way from functional specifications as the only answer.

Many decisions are required throughout the software development process. These decisions, and to some extent the decision-making process itself, can best be documented as the rationale for the system, which will reveal not only what was done during development but the reasons behind the choices made and alternatives considered and rejected. This information becomes increasingly critical as software development becomes more distributed and encompasses the corporate knowledge both used and refined during the development process. The capture of rationale helps to ensure that decisions are well thought out and justified and the use of rationale can help avoid the mistakes of the past during both the development of the current system and when software products (architecture and design, as well as code) are reused in future systems. Burge, Carroll, McCall, and Mistrík describe in detail the capture and use of design rationale in software engineering to improve the quality of software. Their book is the first comprehensive and unified treatment of rationale usage in software engineering. It provides a consistent conceptual framework and a unified terminology for comparing, contrasting and combining the myriad approaches to rationale in software engineering. It is both an excellent introductory text for those new to the field and a uniquely valuable reference for experienced rationale researchers. The book covers the use of rationale for decision making throughout the software lifecycle, starting from the first decisions in a project and continuing through requirements definition, design, implementation, testing, maintenance, redesign and reuse.

Thirty years ago, I first entered the dark realm of software engineering, through a prior interest in documentation. In those days, documentation pretty much meant functional specifications. The idea that stakeholders in a system (its implementers, its end-users, its maintainers, and so forth) might want something other than an alphabetic list of function definitions was just taking hold. There was an exciting (to me) vision of stakeholders accessing and contributing to explanations of how and why aspects of a system work as they do, tradeoff analysis of concomitant downsides, and perhaps even accounts of why other possible approaches were not followed. There were many challenges to overcome in achieving this vision. The most formidable is the belief that people do not like to create or use do- mentation. This negative image of documentation is (unfortunately) more than just the bias of a few incorrigible system developers. It is more like a deep truth about human information behavior, about how human beings construe and act towards information. Humans are, by default, active users of information; they want to try things out, and get things done. When documentation is interposed as a prerequisite between people and a desired activity, they try to skip through it, circumvent it, or undermine it. Desi- ing information to suit the needs and interests of its users is an abiding challenge, but we have come a long way from functional specifications as the only answer.

Many decisions are required throughout the software development process. These decisions, and to some extent the decision-making process itself, can best be documented as the rationale for the system, which will reveal not only what was done during development but the reasons behind the choices made and alternatives considered and rejected. This information becomes increasingly critical as software development becomes more distributed and encompasses the corporate knowledge both used and refined during the development process. The capture of rationale helps to ensure that decisions are well thought out and justified and the use of rationale can help avoid the mistakes of the past during both the development of the current system and when software products (architecture and design, as well as code) are reused in future systems. Burge, Carroll, McCall, and Mistrík describe in detail the capture and use of design rationale in software engineering to improve the quality of software. Their book is the first comprehensive and unified treatment of rationale usage in software engineering. It provides a consistent conceptual framework and a unified terminology for comparing, contrasting and combining the myriad approaches to rationale in software engineering. It is both an excellent introductory text for those new to the field and a uniquely valuable reference for experienced rationale researchers. The book covers the use of rationale for decision making throughout the software lifecycle, starting from the first decisions in a project and continuing through requirements definition, design, implementation, testing, maintenance, redesign and reuse.

Collaboration among individuals – from users to developers – is central to modern software engineering. It takes many forms: joint activity to solve common problems, negotiation to resolve conflicts, creation of shared definitions, and both social and technical perspectives impacting all software development activity. The difficulties of collaboration are also well documented. The grand challenge is not only to ensure that developers in a team deliver effectively as individuals, but that the whole team delivers more than just the sum of its parts.The editors of this book have assembled an impressive selection of authors, who have contributed to an authoritative body of work tackling a wide range of issues in the field of collaborative software engineering. The resulting volume is divided into four parts, preceded by a general editorial chapter providing a more detailed review of the domain of collaborative software engineering. Part 1 is on "Characterizing Collaborative Software Engineering", Part 2 examines various "Tools and Techniques", Part 3 addresses organizational issues, and finally Part 4 contains four examples of "Emerging Issues in Collaborative Software Engineering".As a result, this book delivers a comprehensive state-of-the-art overview and empirical results for researchers in academia and industry in areas like software process management, empirical software engineering, and global software development. Practitioners working in this area will also appreciate the detailed descriptions and reports which can often be used as guidelines to improve their daily work.

Why have a book about the relation between requirements and software architecture? Understanding the relation between requirements and architecture is important because the requirements, be they explicit or implicit, represent the function, whereas the architecture determines the form. While changes to a set of requirements may impact on the realization of the architecture, choices made for an architectural solution may impact on requirements, e.g., in terms of revising functional or non-functional requirements that cannot actually be met.Although research in both requirements engineering and software architecture is quite active, it is in their combination that understanding is most needed and actively sought. Presenting the current state of the art is the purpose of this book. The editors have divided the contributions into four parts: Part 1 “Theoretical Underpinnings and Reviews” addresses the issue of requirements change management in architectural design through traceability and reasoning. Part 2 “Tools and Techniques” presents approaches, tools, and techniques for bridging the gap between software requirements and architecture. Part 3 “Industrial Case Studies” then reports industrial experiences, while part 4 on “Emerging Issues” details advanced topics such as synthesizing architecture from requirements or the role of middleware in architecting for non-functional requirements. The final chapter is a conclusions chapter identifying key contributions and outstanding areas for future research and improvement of practice.The book is targeted at academic and industrial researchers in requirements engineering or software architecture. Graduate students specializing in these areas as well as advanced professionals in software development will also benefit from the results and experiences presented in thisvolume.

Collaboration among individuals – from users to developers – is central to modern software engineering. It takes many forms: joint activity to solve common problems, negotiation to resolve conflicts, creation of shared definitions, and both social and technical perspectives impacting all software development activity. The difficulties of collaboration are also well documented. The grand challenge is not only to ensure that developers in a team deliver effectively as individuals, but that the whole team delivers more than just the sum of its parts.The editors of this book have assembled an impressive selection of authors, who have contributed to an authoritative body of work tackling a wide range of issues in the field of collaborative software engineering. The resulting volume is divided into four parts, preceded by a general editorial chapter providing a more detailed review of the domain of collaborative software engineering. Part 1 is on "Characterizing Collaborative Software Engineering", Part 2 examines various "Tools and Techniques", Part 3 addresses organizational issues, and finally Part 4 contains four examples of "Emerging Issues in Collaborative Software Engineering".As a result, this book delivers a comprehensive state-of-the-art overview and empirical results for researchers in academia and industry in areas like software process management, empirical software engineering, and global software development. Practitioners working in this area will also appreciate the detailed descriptions and reports which can often be used as guidelines to improve their daily work.