Generating a Performance Stochastic Model from UML Specifications

Since its initiation by Connie Smith, the process of Software Performance Engineering (SPE) is becoming a growing concern. The idea is to bring performance evaluation into the software design process. This suitable methodology allows software designers to determine the performance of software during design. Several approaches have been proposed to provide such techniques. Some of them propose to derive from a UML (Unified Modeling Language) model a performance model such as Stochastic Petri Net (SPN) or Stochastic process Algebra (SPA) models. Our work belongs to the same category. We propose to derive from a UML model a Stochastic Automata Network (SAN) in order to obtain performance predictions. Our approach is more flexible due to the SAN modularity and its high resemblance to UML' state-chart diagram

A coalgebraic semantics for causality in Petri nets

In this paper we revisit some pioneering efforts to equip Petri nets with compact operational models for expressing causality. The models we propose have a bisimilarity relation and a minimal representative for each equivalence class, and they can be fully explained as coalgebras on a presheaf category on an index category of partial orders. First, we provide a set-theoretic model in the form of a a causal case graph, that is a labeled transition system where states and transitions represent markings and firings of the net, respectively, and are equipped with causal information. Most importantly, each state has a poset representing causal dependencies among past events. Our first result shows the correspondence with behavior structure semantics as proposed by Trakhtenbrot and Rabinovich. Causal case graphs may be infinitely-branching and have infinitely many states, but we show how they can be refined to get an equivalent finitely-branching model. In it, states are equipped with symmetries, which are essential for the existence of a minimal, often finite-state, model. The next step is constructing a coalgebraic model. We exploit the fact that events can be represented as names, and event generation as name generation. Thus we can apply the Fiore-Turi framework: we model causal relations as a suitable category of posets with action labels, and generation of new events with causal dependencies as an endofunctor on this category. Then we define a well-behaved category of coalgebras. Our coalgebraic model is still infinite-state, but we exploit the equivalence between coalgebras over a class of presheaves and History Dependent automata to derive a compact representation, which is equivalent to our set-theoretical compact model. Remarkably, state reduction is automatically performed along the equivalence.Comment: Accepted by Journal of Logical and Algebraic Methods in Programmin

Timed Automata Semantics for Analyzing Creol

We give a real-time semantics for the concurrent, object-oriented modeling language Creol, by mapping Creol processes to a network of timed automata. We can use our semantics to verify real time properties of Creol objects, in particular to see whether processes can be scheduled correctly and meet their end-to-end deadlines. Real-time Creol can be useful for analyzing, for instance, abstract models of multi-core embedded systems. We show how analysis can be done in Uppaal.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499

Data-driven Modeling and Coordination of Large Process Structures

In the engineering domain, the development of complex products (e.g., cars) necessitates the coordination of thousands of (sub-)processes. One of the biggest challenges for process management systems is to support the modeling, monitoring and maintenance of the many interdependencies between these sub-processes. The resulting process structures are large and can be characterized by a strong relationship with the assembly of the product; i.e., the sub-processes to be coordinated can be related to the different product components. So far, sub-process coordination has been mainly accomplished manually, resulting in high efforts and inconsistencies. IT support is required to utilize the information about the product and its structure for deriving, coordinating and maintaining such data-driven process structures. In this paper, we introduce the COREPRO framework for the data-driven modeling of large process structures. The approach reduces modeling efforts significantly and provides mechanisms for maintaining data-driven process structures