ModGraph meets Xcore: Combining Rule-Based and Procedural Behavioral Modeling for EMF

Model-driven software engineering aims at increasing productivity bydeveloping high-level executable models. The Eclipse Modeling Framework (EMF)significantly contributes toward this goal. Unfortunately, EMF supports only structural models based on the Ecore metamodel. Recently, Xcore has been developed to extend EMF with behavioral modeling. To this end, Xcore provides a single textual language for both structural and behavioral modeling. While Xcore follows a procedural approach to behavioral modeling, ModGraph is an EMF-based tool based on a rule-based paradigm (graph transformation rules, which allow to specify behavior in a declarative way). The combination of EMF, Xcore, and ModGraph results in an environment for model-driven software engineering which provides full-fledged support for both structural and behavioral modeling. Altogether, we obtain an environment in which software engineers are concerned only with models rather than with programs

A Dual-Engine for Early Analysis of Critical Systems

This paper presents a framework for modeling, simulating, and checking properties of critical systems based on the Alloy language -- a declarative, first-order, relational logic with a built-in transitive closure operator. The paper introduces a new dual-analysis engine that is capable of providing both counterexamples and proofs. Counterexamples are found fully automatically using an SMT solver, which provides a better support for numerical expressions than the existing Alloy Analyzer. Proofs, however, cannot always be found automatically since the Alloy language is undecidable. Our engine offers an economical approach by first trying to prove properties using a fully-automatic, SMT-based analysis, and switches to an interactive theorem prover only if the first attempt fails. This paper also reports on applying our framework to Microsoft's COM standard and the mark-and-sweep garbage collection algorithm.Comment: Workshop on Dependable Software for Critical Infrastructures (DSCI), Berlin 201

VERTO: a visual notation for declarative process models

Declarative approaches to business process modeling allow to represent loosely-structured (declarative) processes in flexible scenarios as a set of constraints on the allowed flow of activities. However, current graphical notations for declarative processes are difficult to interpret. As a consequence, this has affected widespread usage of such notations, by increasing the dependency on experts to understand their semantics. In this paper, we tackle this issue by introducing a novel visual declarative notation targeted to a more understandable modeling of declarative processes

Enhancing declarative process models with DMN decision logic

Modeling dynamic, human-centric, non-standardized and knowledge-intensive business processes with imperative process modeling approaches is very challenging. Declarative process modeling approaches are more appropriate for these processes, as they offer the run-time flexibility typically required in these cases. However, by means of a realistic healthcare process that falls in the aforementioned category, we demonstrate in this paper that current declarative approaches do not incorporate all the details needed. More specifically, they lack a way to model decision logic, which is important when attempting to fully capture these processes. We propose a new declarative language, Declare-R-DMN, which combines the declarative process modeling language Declare-R with the newly adopted OMG standard Decision Model and Notation. Aside from supporting the functionality of both languages, Declare-R-DMN also creates bridges between them. We will show that using this language results in process models that encapsulate much more knowledge, while still offering the same flexibility

Declarative Modeling and Bayesian Inference of Dark Matter Halos

Probabilistic programming allows specification of probabilistic models in a declarative manner. Recently, several new software systems and languages for probabilistic programming have been developed on the basis of newly developed and improved methods for approximate inference in probabilistic models. In this contribution a probabilistic model for an idealized dark matter localization problem is described. We first derive the probabilistic model for the inference of dark matter locations and masses, and then show how this model can be implemented using BUGS and Infer.NET, two software systems for probabilistic programming. Finally, the different capabilities of both systems are discussed. The presented dark matter model includes mainly non-conjugate factors, thus, it is difficult to implement this model with Infer.NET.Comment: Presented at the Workshop "Intelligent Information Processing", EUROCAST2013. To appear in selected papers of Computer Aided Systems Theory - EUROCAST 2013; Volumes Editors: Roberto Moreno-D\'iaz, Franz R. Pichler, Alexis Quesada-Arencibia; LNCS Springe