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Concern identification aims to find the implementation of a functional concern in existing source code. In this work, concerns are described, using the Hierarchic Concern Model, as gray-boxes containing subconcerns, inputs, and outputs. The inputs and outputs are used as concern seeds to identify data-oriented abstractions of concern implementations, called concern skeletons. The identification approach is based on context free language reachability and supported by a tool, called CoDEx.
In component-based software engineering, performance prediction approaches support the design of business information systems on the architectural level. They are based on behavior specifications of components. This work presents a round-trip approach for using, assessing, and certifying the accuracy of parameterized, probabilistic, deterministic, and concurrent performance specifications. Its applicability and effectiveness are demonstrated using the CoCoME benchmark.
This dissertation thesis presents an approach enabling the modelling and quality-of-service prediction of event-based systems at the architecture-level. Applying a two-step model refinement transformation, the approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques.
This book provides formal and informal definitions and taxonomies for self-aware computing systems, and explains how self-aware computing relates to many existing subfields of computer science, especially software engineering. It describes architectures and algorithms for self-aware systems as well as the benefits and pitfalls of self-awareness, and reviews much of the latest relevant research across a wide array of disciplines, including open research challenges. The chapters of this book are organized into five parts: Introduction, System Architectures, Methods and Algorithms, Applications and Case Studies, and Outlook. Part I offers an introduction that defines self-aware computing system...
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 tutorial book presents an augmented selection of material presented at the International Summer School on Generative and Transformational Techniques in Software Engineering, GTTSE 2005. The book comprises 7 tutorial lectures presented together with 8 technology presentations and 6 contributions to the participants workshop. The tutorials combine foundations, methods, examples, and tool support. Subjects covered include feature-oriented programming and the AHEAD tool suite; program transformation with reflection and aspect-oriented programming, and more.
This work introduces the FURCAS approach, a framework for view-based textual modelling. FURCAS includes means that allow software language engineers to define partial and overlapping textual modelling languages. Furthermore, FURCAS provides an incremental update approach that enables modellers to work with multiple views on the same underlying model. The approach is validated against a set of formal requirements, as well as several industrial case studies showing its practical applicability.
In software development, project constraints lead to customer-specific variants by copying and adapting the product. During this process, modifications are scattered all over the code. Although this is flexible and efficient in the short term, a Software Product Line (SPL) offers better results in the long term, regarding cost reduction, time-to-market, and quality attributes. This book presents a novel approach named SPLevo, which consolidates customized product copies into an SPL.
Model-based performance prediction systematically deals with the evaluation of software performance to avoid for example bottlenecks, estimate execution environment sizing, or identify scalability limitations for new usage scenarios. Such performance predictions require up-to-date software performance models. This book describes a new integrated reverse engineering approach for the reconstruction of parameterised software performance models (software component architecture and behaviour).
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