Model Driven Software Engineering Meta-Workbenches: An XTools Approach

Model Driven Software Engineering aims to provide a quality assured process for designing and generating software. Modelling frameworks that offer technologies for domain specific language and associated tool construction are called language workbenches. Since modelling is itself a domain, there are benefits to applying a workbenchbased approach to the construction of modelling languages and tools. Such a framework is a meta-modelling tool and those that can generate themselves are reflective metatools. This article reviews the current state of the art for modelling tools and proposes a set of reflective meta-modelling tool requirements. The XTools framework has been designed as a reflective meta-tool and is used as a benchmark

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

Meta-modeling model-based engineering tools (Dagstuhl Seminar 13182)

Model-based engineering (MBE) is a software development approach in which abstraction via modeling is used as the primary mechanism for managing the complexity of software-based systems. An effective approach to software development must be supported by effective technologies (i.e., languages, methods, processes, tools). The wide range of development tasks that effective MBE approaches must support leads to two possible tooling scenarios. In the first scenario a federated collection of tools is used to support system development. Each tool in the collection provides specialized services. Tool interoperability and consistency of information across the tools are major concerns in this scenario. These concerns are typically addressed using transformations and exposed tool interfaces. Defining and evolving the transformations and interfaces requires detailed low-level knowledge of the tools and thus leads to complex tooling environments that are difficult to configure, learn, use, and evolve. In the second scenario, a single tool is used to support the complete modeling lifecycle. This avoids the inter-tool transformation and consistency problems, but the resulting multi-featured tool is a monolithic entity that is costly to develop and evolve. Furthermore, the large number of non-trivial features can make learning and using such tools difficult. Successful uptake of MDE in industry requires supporting tools to be, at least, useful and usable. From a tool developer's perspective, there is also a need to significantly reduce the cost and effort required to develop and evolve complex MBE tools. This seminar brings together experts in the areas of MBE, meta-modeling, tool development, and human-computer interactions to map out a research agenda that lays a foundation for the development of effective MBE tools. Such a foundation will need to support not only interoperability of tools or tool features, but also the implementation of high quality MBE tools. The long-term objective is to foster a research community that will work on a foundation that can be expressed in the form of standard tool (meta-)models that capture and leverage high quality reusable MBE tool development experience

Meta-modeling model-based engineering tools (Dagstuhl Seminar 13182)

Model-based engineering (MBE) is a software development approach in which abstraction via modeling is used as the primary mechanism for managing the complexity of software-based systems. An effective approach to software development must be supported by effective technologies (i.e., languages, methods, processes, tools). The wide range of development tasks that effective MBE approaches must support leads to two possible tooling scenarios. In the first scenario a federated collection of tools is used to support system development. Each tool in the collection provides specialized services. Tool interoperability and consistency of information across the tools are major concerns in this scenario. These concerns are typically addressed using transformations and exposed tool interfaces. Defining and evolving the transformations and interfaces requires detailed low-level knowledge of the tools and thus leads to complex tooling environments that are difficult to configure, learn, use, and evolve. In the second scenario, a single tool is used to support the complete modeling lifecycle. This avoids the inter-tool transformation and consistency problems, but the resulting multi-featured tool is a monolithic entity that is costly to develop and evolve. Furthermore, the large number of non-trivial features can make learning and using such tools difficult. Successful uptake of MDE in industry requires supporting tools to be, at least, useful and usable. From a tool developer's perspective, there is also a need to significantly reduce the cost and effort required to develop and evolve complex MBE tools. This seminar brings together experts in the areas of MBE, meta-modeling, tool development, and human-computer interactions to map out a research agenda that lays a foundation for the development of effective MBE tools. Such a foundation will need to support not only interoperability of tools or tool features, but also the implementation of high quality MBE tools. The long-term objective is to foster a research community that will work on a foundation that can be expressed in the form of standard tool (meta-)models that capture and leverage high quality reusable MBE tool development experience

On the connection of probabilistic model checking, planning, and learning for system verification

This thesis presents approaches using techniques from the model checking, planning, and learning community to make systems more reliable and perspicuous. First, two heuristic search and dynamic programming algorithms are adapted to be able to check extremal reachability probabilities, expected accumulated rewards, and their bounded versions, on general Markov decision processes (MDPs). Thereby, the problem space originally solvable by these algorithms is enlarged considerably. Correctness and optimality proofs for the adapted algorithms are given, and in a comprehensive case study on established benchmarks it is shown that the implementation, called Modysh, is competitive with state-of-the-art model checkers and even outperforms them on very large state spaces. Second, Deep Statistical Model Checking (DSMC) is introduced, usable for quality assessment and learning pipeline analysis of systems incorporating trained decision-making agents, like neural networks (NNs). The idea of DSMC is to use statistical model checking to assess NNs resolving nondeterminism in systems modeled as MDPs. The versatility of DSMC is exemplified in a number of case studies on Racetrack, an MDP benchmark designed for this purpose, flexibly modeling the autonomous driving challenge. In a comprehensive scalability study it is demonstrated that DSMC is a lightweight technique tackling the complexity of NN analysis in combination with the state space explosion problem.Diese Arbeit präsentiert Ansätze, die Techniken aus dem Model Checking, Planning und Learning Bereich verwenden, um Systeme verlässlicher und klarer verständlich zu machen. Zuerst werden zwei Algorithmen für heuristische Suche und dynamisches Programmieren angepasst, um Extremwerte für Erreichbarkeitswahrscheinlichkeiten, Erwartungswerte für Kosten und beschränkte Varianten davon, auf generellen Markov Entscheidungsprozessen (MDPs) zu untersuchen. Damit wird der Problemraum, der ursprünglich mit diesen Algorithmen gelöst wurde, deutlich erweitert. Korrektheits- und Optimalitätsbeweise für die angepassten Algorithmen werden gegeben und in einer umfassenden Fallstudie wird gezeigt, dass die Implementierung, namens Modysh, konkurrenzfähig mit den modernsten Model Checkern ist und deren Leistung auf sehr großen Zustandsräumen sogar übertrifft. Als Zweites wird Deep Statistical Model Checking (DSMC) für die Qualitätsbewertung und Lernanalyse von Systemen mit integrierten trainierten Entscheidungsgenten, wie z.B. neuronalen Netzen (NN), eingeführt. Die Idee von DSMC ist es, statistisches Model Checking zur Bewertung von NNs zu nutzen, die Nichtdeterminismus in Systemen, die als MDPs modelliert sind, auflösen. Die Vielseitigkeit des Ansatzes wird in mehreren Fallbeispielen auf Racetrack gezeigt, einer MDP Benchmark, die zu diesem Zweck entwickelt wurde und die Herausforderung des autonomen Fahrens flexibel modelliert. In einer umfassenden Skalierbarkeitsstudie wird demonstriert, dass DSMC eine leichtgewichtige Technik ist, die die Komplexität der NN-Analyse in Kombination mit dem State Space Explosion Problem bewältigt

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book