Creating product line architectures

The creation and validation of product line software architectures are inherently more complex than those of software architectures for single systems. This paper compares a process for creating and evaluating a traditional, one-of-a- kind software architecture with one for a reference software architecture. The comparison is done in the context of PuLSE-DSSA, a customizable process that integrates both product line architecture creation and evaluation

Applied metamodelling : a foundation for language driven development

The Third Edition of Applied Metamodelling represents a small increment since the Second Edition was produced in 2008. The book continues to be referenced in schol- arly articles with 212 citations on Google Scholar including nearly 150 since 2008. The open-source release of the accompanying technologies XMF and XMF-Mosaic did not generate the same level of interest, partly due to a lack of exposure and associated tuto- rial materials. Recently, interest in the field of multi-level modelling has increased and has led to publications and a Dagstuhl Seminar based on the ideas of meta-languages and tool-modelling. An overview of the historical development of XMF, the ideas in this book, and the the birth and death of an associated startup company has been published in 2012 as part of the 10th anniversary edition of the the Journal of Software and Systems Modeling. A project based on the foundations developed in this book and the tooling, now rebranded XModeler, is underway and the aim is to advance the field of multi-level language-based system engineering

Applied metamodelling: a foundation for language driven development.

The motivation behind XMF was to develop a technology that would support a language driven approach to modelling and system development. Our starting point was that UML was not sufficiently flexible and in order to address this we designed an executable meta-language. This book describes the approach and provides an introduction to the key technologies: meta-models, language definition and mappings. The book concludes with a number of worked examples

The Essence of Software Engineering

Software Engineering; Software Development; Software Processes; Software Architectures; Software Managemen

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

Agent-based methodology for developing agroecosystems simulations

The agent-based modeling (ABM) approach allows modeling complex systems, involving different kinds of interacting autonomous agents with heterogeneous behavior. Agro-ecosystems (ecological systems subject to human interaction) are a kind of complex system whose analysis and simulation is of interest to several disciplines (e.g. agronomy, ecology or sociology). In this context, the ABM approach appears as a suitable tool for modeling agro-ecosystems, along with a corresponding agent-oriented software engineering (AOSE) methodology for the construction of the simulation. Nevertheless, existing AOSE methodologies are general-purpose, they have not yet accomplished widespread use, and clear examples of applications to agro-ecosystems are hard to find. This thesis sets the ground for a new software development methodology for developing agro-ecosystem simulations based on the ABM approach as well as on these already existing AOSE methodologies, but tailored to tackle specific agro-ecosystem features.El enfoque de modelado basado en agentes (ABM) permite el modelado de sistemas complejos en los que interactúan diferentes tipos de agentes autónomos con comportamientos heterogéneos. Los agro-ecosistemas (sistemas ecológicos sujetos a la presencia humana) son un tipo de sistema complejo cuyo análisis y simulación resulta de interés para diversas disciplinas (ej.: agronomía, ecología o sociología). En este contexto, el enfoque ABM aparece como una herramienta adecuada para el modelado de agro-ecosistemas, junto con una correspondiente metodología de desarrollo de software también orientada a agentes (AOSE) para la construcción de dicha simulación. Si bien ya existen metodologías AOSE, éstas son de propósito general, no han logrado un amplio uso y ejemplos claros de aplicaciones a agro-ecosistemas son difíciles de encontrar. Esta tesis establece los fundamentos para crear una nueva metodología de desarrollo de software basada en el enfoque de agentes para el desarrollo de simulaciones de agro-ecosistemas, basándose en las metodologías AOSE ya existentes, pero personalizada para soportar las características específicas de los agro-ecosistemas

Configuration management for models : generic methods for model comparison and model co-evolution

It is an undeniable fact that software plays an important role in our lives. We use the software to play our music, to check our e-mail, or even to help us drive our car. Thus, the quality of software directly influences the quality of our lives. However, the traditional Software Engineering paradigm is not able to cope with the increasing demands in quantity and quality of produced software. Thus, a new paradigm of Model Driven Software Engineering (MDSE) is quickly gaining ground. MDSE promises to solve some of the problems of traditional Software Engineering (SE) by raising the level of abstraction. Thus, MDSE proposes the use of models and model transformations, instead of textual program files used in traditional SE, as means of producing software. The models are usually graph-based, and are built by using graphical notations – i.e. the models are represented diagrammatically. The advantages of using graphical models over text files are numerous, for example it is usually easier to deduce the relations between different model elements in their diagrammatic form, thus reducing the possibility of defects during the production of the software. Furthermore, formal model transformations can be used to produce different kinds of artifacts from models in all stages of software production. For example, artifacts that can be used as input for model checkers or simulation tools can be produced. This enables the checking or simulation of software products in the early phases of development, which further reduces the probability of defects in the final software product. However, methods and techniques to support MDSE are still not mature enough. In particular methods and techniques for model configuration management (MCM) are still in development, and no generic MCM system exists. In this thesis, I describe my research which was focused on developing methods and techniques to support generic model configuration management. In particular, during my research, I focused on developing methods and techniques for supporting model evolution and model co-evolution. Described methods and techniques are generic and are suitable for a state-based approach to model configuration management. In order to support the model evolution, I developed methods for the representation, calculation, and visualization of state-based model differences. Unlike in previously published research, where these three aspects of model differences are dealt with in separation, in my research all these three aspects are integrated. Thus, the result of model differences calculation algorithm is in the format which is described by my research on model differences representation. The same representation format of model differences is used as a basis of my approach to differences visualization. It is important to notice that the developed representation format for model differences is metamodel independent, and thus is generic, i.e., it can be used to represent differences between all graph-based models. Model co-evolution is a term that describes the problem of adapting models when their metamodels evolve. My solution to this problem has three steps. In the first step a special metamodel MMfMM is introduced. Unlike in traditional approaches, where metamodels are represented as instances of a metametamodel, in my approach the metamodels are represented by models which are instances of an MMfMM. In the second step, since metamodels are represented by models, previously defined methods and techniques for model evolution are reused to represent and calculate the metamodel differences. In the final step I define an algorithm that uses the calculated metamodel differences to adapt models conforming to the evolved metamodel. In order to validate my approaches to model evolution and model co-evolution, I have developed a tool for comparing models and visualizing resulting differences, and a tool for model co-evolution. Moreover, I have developed a method to compare tools for model comparison, and using this method I have conducted a series of experiments in which I compared the tool I developed to an industrial tool called EMFCompare. The results of these experiments are also presented in the thesis. Furthermore, in order to validate my tool and approach to model co-evolution, I have also specified and conducted several experiments. The results of these experiments are also presented in the thesis

Domain specific modeling and analysis

It is desirable to model software systems in such a way that analysis of the systems, and tool development for such analysis, is readily possible and feasible in the context of large scientific research projects. This thesis emphasizes the methodology that serves as a basis for such developments. I focus on methods for the design of data-languages and their corresponding tools.UBL - phd migration 201