On behalf of the Organizing Committee we are pleased to present the p- ceedings of the 2008 Symposium on Component-Based Software Engineering (CBSE). CBSE is concerned with the development of software-intensivesystems from independently developed software-building blocks (components), the - velopment of components, and system maintenance and improvement by means of component replacement and customization. CBSE 2008 was the 11th in a series of events that promote a science and technology foundation for achieving predictable quality in software systems through the use of software component technology and its associated software engineering practices. Wewerefortunateto haveadedicatedProgramCommitteecomprisingmany internationallyrecognizedresearchersandindustrialpractitioners.Wewouldlike to thank the members of the Program Committee and associated reviewers for their contribution in making this conference a success. We received 70 subm- sions and each paper was reviewed by at least three Program Committee m- bers (four for papers with an author on the Program Committee). The entire reviewing process was supported by the Conference Management Toolkit p- vided by Microsoft. In total, 20 submissions were accepted as full papers and 3 submissions were accepted as short papers.

This book constitutes the refereed proceedings of the 12th International Symposium on Search-Based Software Engineering, SSBSE 2020, held in Bari, Italy, in October 2020. The 13 research papers and 5 short papers presented together with 1 keynote were carefully reviewed and selected from 34 submissions. SBSE is a research area focused on the formulation of software engineering problems as search problems, and the subsequent use of complex heuristic techniques to attain optimal solutions to such problems. A wealth of engineering challenges - from test generation, to design refactoring, to process organization - can be solved efficiently through the application of automated optimization techniques. SBSE is a growing field - sitting at the crossroads between AI, machine learning, and software engineering - and SBSE techniques have begun to attain human-competitive results. Due to the Corona pandemic SSBSE 2020 was held as a virtual event.

This practical text/reference provides an exhaustive guide to setting up and sustaining software-defined data centers (SDDCs). Each of the core elements and underlying technologies are explained in detail, often supported by real-world examples. The text illustrates how cloud integration, brokerage, and orchestration can ensure optimal performance and usage of data resources, and what steps are required to secure each component in a SDDC. The coverage also includes material on hybrid cloud concepts, cloud-based data analytics, cloud configuration, enterprise DevOps and code deployment tools, and cloud software engineering. Topics and features: highlights how technologies relating to cloud computing, IoT, blockchain, and AI are revolutionizing business transactions, operations, and analytics; introduces the concept of Cloud 2.0, in which software-defined computing, storage, and networking are applied to produce next-generation cloud centers; examines software-defined storage for storage virtualization, covering issues of cloud storage, storage tiering, and deduplication; discusses software-defined networking for network virtualization, focusing on techniques for network optimization in data centers; reviews the qualities and benefits of hybrid clouds, that bridge private and public cloud environments; investigates the security management of a software-defined data center, and proposes a framework for managing hybrid IT infrastructure components; describes the management of multi-cloud environments through automated tools, and cloud brokers that aim to simplify cloud access, use and composition; covers cloud orchestration for automating application integration, testing, infrastructure provisioning, software deployment, configuration, and delivery. This comprehensive work is an essential reference for all practitioners involved with software-defined data center technologies, hybrid clouds, cloud service management, cloud-based analytics, and cloud-based software engineering.

Poor performance is one of the main quality-related shortcomings that cause software projects to fail. Thus, the need to address performance concerns early during the software development process is fully acknowledged, and there is a growing interest in the research and software industry communities towards techniques, methods and tools that permit to manage system performance concerns as an integral part of software engineering. Model-based software performance analysis introduces performance concerns in the scope of software modeling, thus allowing the developer to carry on performance analysis throughout the software lifecycle. With this book, Cortellessa, Di Marco and Inverardi provide the cross-knowledge that allows developers to tackle software performance issues from the very early phases of software development. They explain the basic concepts of performance analysis and describe the most representative methodologies used to annotate and transform software models into performance models. To this end, they go all the way from performance primers through software and performance modeling notations to the latest transformation-based methodologies. As a result, their book is a self-contained reference text on software performance engineering, from which different target groups will benefit: professional software engineers and graduate students in software engineering will learn both basic concepts of performance modeling and new methodologies; while performance specialists will find out how to investigate software performance model building.

The maintenance of long-living software systems is an essential topic in today’s software engineering practice and research. Software Architecture Restructuring is an important task to adjust these systems to current requirements and to keep them maintainable. Niels Streekmann introduces an approach to Software Architecture Restructuring that semi-automates this task by introducing graph clustering. The approach provides an iterative process that systematically incorporates human architectural knowledge for the improvement of the restructuring result. Thus, it supports the task of planning the transfer of an existing system to a target architecture and aims at reducing the required manual effort.