Quality attributes, such as performance or reliability, are crucial for the success of a software system and largely influenced by the software architecture. Their quantitative prediction supports systematic, goal-oriented software design and forms a base of an engineering approach to software design. This thesis proposes a method and tool to automatically improve component-based software architecture (CBA) models based on such quantitative quality prediction techniques.

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.

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.

The software execution environment can play a crucial role when analyzing the performance of a software system. In this book, a novel approach for the automated detection of performance-relevant properties of the execution environment is presented. The properties are detected using predefined experiments and integrated into performance prediction tools. The approach is applied to experiments for detecting different CPU, OS, and virtualization properties, and validated in different case studies.

This book constitutes the refereed proceedings of the 12th European Conference on Software Architecture, ECSA 2018, held in Madrid, Spain, in September 2018. The 17 full papers presented together with 7 short papers were carefully reviewed and selected from 96 submissions. They are organized in topical sections as follows: Self-Adaptive Architectures, IoT Architectures, Embedded and Cyber-Physical Systems, Microservices Architectures, Service-Oriented Architectures, Architectural Design Decisions, Software Architecture in Practice.