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When complex IT systems are being developed, the usage of several programming and modelling languages can lead to inconsistencies that yield faulty designs and implementations. To address this problem, this work contributes a classification of consistency preservation challenges and an approach for preserving consistency. It is formalized using set theory and monitors changes to avoid matching and diffing problems. Three new languages that follow this preservation approach are presented.
Complex software systems are described with multiple artifacts, such as code, design diagrams and others. Ensuring their consistency is crucial and can be automated with transformations for pairs of artifacts. We investigate how developers can combine independently developed and reusable transformations to networks that preserve consistency between more than two artifacts. We identify synchronization, compatibility and orchestration as central challenges, and we develop approaches to solve them.
When models of a system change, analyses based on them have to be reevaluated in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. This thesis proposes multiple, combinable approaches and a new formalism based on category theory for implicitly incremental model analyses and transformations. The advantages of the implementation are validated using seven case studies, partially drawn from the Transformation Tool Contest (TTC).
Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems.
This book contains the thoroughly refereed technical papers presented in eight workshops collocated with the International Conference on Software Technologies: Applications and Foundations, STAF 2018, held in Toulouse, France, in June 2018. The 65 full papers presented were carefully reviewed and selected from 120 submissions. The events whose papers are included in this volume are: CoSim-CPS 2018: 2nd International Workshop on Formal Co-Simulation of Cyber-Physical Systems DataMod 2018: 7th International Symposium From Data to Models and Back FMIS 2018: 7th International Workshop on Formal Methods for Interactive Systems FOCLASA 2018: 16th International Workshop on Foundations of Coordination Languages and Self-adaptative Systems GCM 2018: 9th International Workshop on Graph Computation Models MDE@DeRun 2018: 1st International Workshop on Model-Driven Engineering for Design-Runtime Interaction in Complex Systems MSE 2018: 3rd International Workshop on Microservices: Science and Engineering SecureMDE 2018: 1st International Workshop on Security for and by Model-Driven Engineering
In this work, the authors analysed the co-dependency between models and analyses, particularly the structure and interdependence of artefacts and the feature-based decomposition and composition of model-based analyses. Their goal is to improve the maintainability of model-based analyses. They have investigated the co-dependency of Domain-specific Modelling Languages (DSMLs) and model-based analyses regarding evolvability, understandability, and reusability.
Die modellbasierte Performancevorhersage ist ein bekanntes Konzept zur Gewährleistung der Softwarequalität. Derzeitige Ansätze basieren auf einem Modell mit einer Metrik, was zu ungenauen Vorhersagen für moderne Architekturen führt. In dieser Arbeit wird ein Multi-Strategie-Ansatz zur Erweiterung von Performancevorhersagemodellen zur Unterstützung von Multicore-Architekturen vorgestellt, in Palladio implementiert und dadurch die Genauigkeit der Vorhersage deutlich verbessert. - Model-based performance prediction is a well-known concept to ensure the quality of software. Current approaches are based on a single-metric model, which leads to inaccurate predictions for modern architectures. This thesis presents a multi-strategies approach to extend performance prediction models to support multicore architectures. We implemented the strategies into Palladio and significantly increased the performance prediction power.
Business processes and information systems evolve constantly and affect each other in non-trivial ways. Aligning security requirements between both is a challenging task. This work presents an automated approach to extract access control requirements from business processes with the purpose of transforming them into a) access permissions for role-based access control and b) architectural data flow constraints to identify violations of access control in enterprise application architectures.