Relating System Quality And Software Architecture

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.

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 this volume.

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

This book constitutes the joint refereed proceedings of two colocated events: the First International Conference on the Quality of Software Architectures (QoSA 2005) and the Second International Workshop on Software Quality (SOQUA 2005) held in Erfurt, Germany, in September 2005. The 18 revised full papers presented were carefully reviewed and selected from 48 submissions. For QoSA 2005 only 12 papers - of the 31 submitted - were accepted for presentation; they are concerned with research and experiences that investigate the influence a specific software architecture has on software quality aspects. The papers are organized in topical sections on software architecture evaluation, formal approaches to model-driven QoS-handling, modelling QoS in software architectures, software architectures applied, architectural design for QoS, and model-driven software reliability estimation. The 6 papers accepted for SOQUA 2005 - from 17 submissions - mainly focus on quality assurance and on software testing. They are organized in topical sections on test case selection, model-based testing, unit testing, and performance testing.

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

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

Models are used in all kinds of engineering disciplines to abstract from the various details of the modelled entity in order to focus on a speci?c aspect. Like a blueprint in civil engineering, a software architecture providesan abstraction from the full software system’s complexity. It allows software designers to get an overview on the system underdevelopmentandtoanalyzeitsproperties.Inthissense,modelsarethefoundation needed for software development to become a true engineering discipline. Especially when reasoning on a software system’s extra-functional properties, its software architecture carries the necessary information for early, design-time analyses. These analyses take the software architecture as input and can be used to direct the design process by allowing a systematic evaluation of different design alternatives. For example, they can be used to cancel out decisions which would lead to architecture - signs whose implementation would not comply with extra-functionalrequirements like performance or reliability constraints. Besides such quality attributes directly visible to the end user, internal quality attributes, e.g., maintainability, also highly depend on the system’s architecture. In addition to the above-mentioned technical aspects of software architecture m- els, non-technical aspects, especially project management-related activities, require an explicit software architecture model. The models are used as input for cost esti- tions, time-, deadline-, and resource planning for the development teams. They serve the project management activities of planning, executing, and controlling, which are necessary to deliver high-quality software systems in time and within the budget.

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.