Taming Graphical Modeling

Visual models help to understand complex systems. However, with the user interaction paradigms established today, activities such as creating, maintaining or browsing visual models can be very tedious. Valuable engineering time is wasted with archaic activities such as manual placement and routing of nodes and edges. This report presents an approach to enhance productivity by focusing on the pragmatics of model-based design. Our contribution is twofold: First, the concept of meta layout enables the synthesis of different diagrammatic views on graphical models. This modularly employs sophisticated layout algorithms, closing the gap between MDE and graph drawing theory. Second, a view management logic harnesses this auto layout to present customized views on models. These concepts have been implemented in the open source Kiel Integrated Environment for Layout Eclipse Rich Client (KIELER). Two applications---editing and simulation---illustrate how view management helps to increase developer productivity and tame model complexity

Executing Domain-Specific Models in Eclipse: KLEPTO - KIELER leveraging Ptolemy

We present a two-level approach to extend the abstract syntax of domain-specific models with concrete semantics in order to execute such models. First, a light-weight execution infrastructure for executable models with a generic user interface allows the tool smith to provide arbitrary execution and visualisation engine implementations for a Domain-Specific Language (DSL). Second, as a concrete but nevertheless generic implementation of a simulation engine for behaviour models, we present semantic model specifications and a runtime interfacing to the Ptolemy II tool suite as a formally founded backbone for model execution. We present our approach as an open source extension to Eclipse modelling projects

Annual Research Report, 2009-2010

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and governmenthttps://digitalcommons.odu.edu/or_researchreports/1001/thumbnail.jp

Annual Report Of Research and Creative Productions, January to December, 2007

2007 Annual Report of Research and Creative Productions, Morehead State University, Division of Academic Affairs, Research and Creative Productions Committee

Innovations for Requirements Analysis, From Stakeholders' Needs to Formal Designs

14th MontereyWorkshop 2007 Monterey, CA, USA, September 10-13, 2007 Revised Selected PapersWe are pleased to present the proceedings of the 14thMontereyWorkshop, which took place September 10–13, 2007 in Monterey, CA, USA. In this preface, we give the reader an overview of what took place at the workshop and introduce the contributions in this Lecture Notes in Computer Science volume. A complete introduction to the theme of the workshop, as well as to the history of the Monterey Workshop series, can be found in Luqi and Kordon’s “Advances in Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and Formal Designs” in this volume. This paper also contains the case study that many participants used as a problem to frame their analyses, and a summary of the workshop’s results

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