Cláudio Gomes

Classification of Model Transformation Tools: Pattern Matching Techniques

While comparing different model transformation languages (MTLs), it is common to refer to their syntactic and semantic features and overlook their supporting tools’ performance. Performance is one of the aspects that can hamper the application of MDD to industrial scenarios. An highly declarative MTL might simply not scale well when using large models due to its supporting implementation. In this paper, we focus on the several pattern matching techniques (including optimization techniques) employed in the most popular transformation tools, and discuss their effectiveness w.r.t. the expressive power of the languages used. Because pattern matching is the most costly operation in a transformation execution, we present a classification of the existing model transformation tools according to the pattern matching optimization techniques they implement. Our classification complements existing ones that are more focused at syntactic and semantic features of the languages supported by those tools.

Towards Modular Language Design Using Language Fragments: The Hybrid Systems Case Study

Cyber-physical systems can be best represented using hybrid models that contain specifications of both continuous and discrete event abstractions. The syntax and semantics of such hybrid languages should ideally be defined by reusing the syntax and semantics of each components’ formalisms. In language composition, semantic adaptation is needed to ensure correct realization of the concepts that are part of the intricacies of the hybrid language.

Modular design of hybrid languages by explicit modeling of semantic adaptation

The engineering of a complex cyber-physical system (CPS) involves the creation and simulation of hybrid models of- ten encompassing multiple levels of abstraction and combining different formalisms, often not expressible in any single existing formalisms. Modular language engineering is thus essential for effective and efficient development of new formalisms, appropriate for the task. In our work, each modeling language is represented as a language specification fragment: a modular representation of the syntax and semantics. We propose a white-box technique for explicitly modeling the definition and composition of the fragments. In this paper, we focus on the composition of the operational semantics (i.e., the semantic adaptation) of hybrid languages. This enables automatic synthesis of a simulator for the hybrid language. Our approach is demonstrated by creating two well-known hybrid languages as a composition of Timed Finite State Automata (TFSA) and Causal Block Diagrams (CBD) - hybrid TFSA and hybrid CBD.

Approximated Stability Analysis of Bi-modal Hybrid Co-simulation Scenarios

Co-simulation is a technique to orchestrate multiple simulators in order to approximate the behavior of a coupled system as a whole. Simulators execute in a lockstep fashion, each exchanging inputs and output data points with the other simulators at pre-accorded times.

Co-Simulation: A Survey

Modeling and simulation techniques are today extensively used both in industry and science. Parts of larger systems are, however, typically modeled and simulated by different techniques, tools, and algorithms. In addition, experts from different disciplines use various modeling and simulation techniques. Both these facts make it difficult to study coupled heterogeneous systems. Co-simulation is an emerging enabling technique, where global simulation of a coupled system can be achieved by composing the simulations of its parts. Due to its potential and interdisciplinary nature, co-simulation is being studied in different disciplines but with limited sharing of findings. In this survey, we study and survey the state-of-the-art techniques for co-simulation, with the goal of enhancing future research and highlighting the main challenges. To study this broad topic, we start by focusing on discrete-event-based co-simulation, followed by continuous-time-based co-simulation. Finally, we explore the interactions between these two paradigms, in hybrid co-simulation. To survey the current techniques, tools, and research challenges, we systematically classify recently published research literature on co-simulation, and summarize it into a taxonomy. As a result, we identify the need for finding generic approaches for modular, stable, and accurate coupling of simulation units, as well as expressing the adaptations required to ensure that the coupling is correct.

Semantic adaptation for FMI co-simulation with hierarchical simulators

Model-based design can shorten the development time of complex systems by the use of simulation techniques. However, it can be hard to simulate the system as a whole if it is developed in a concurrent fashion by multiple and specialized teams. Co-simulation, with the support of the Functional Mockup Interface (FMI) Standard, is proposed as a way to promote tool interoperability while protecting the intellectual property of subsystems. The standard allows uniform communication between subsystem simulators, but does not state how the inputs and outputs should be interpreted, nor how the subsystems should interact correctly. Semantic adaptations can be quickly made to correct the interactions with subsystem simulators that were produced with different assumptions, and avoid changing those subsystems, their simulators, or the orchestration algorithm that computes the co-simulation. In this work, we explore how to describe common adaptations and what their meaning is in the context of FMI co-simulation. The result is a sound language that enables the implementation of adaptations with minimal effort. A distinct feature is that it describes adaptations for groups of interconnected subsystem simulators in the same way as for a single simulator, and the implementation is itself a simulator, thanks to a sound definition of hierarchical co-simulation. This work paves the way for research into the correct combination and interfacing of different adaptations.

A building automation case study setup and challenges

Smart buildings will play a fundamental role in ensuring comfort while reducing the energy required. However, due to the lack of knowledge about the operation of the smart controllers, the occupants can unintentionally increase the energy spent. Nevertheless, there is evidence that the informed and motivated user will actually cooperate with the system. Some of the issues associated with researching control systems in the context of building automation are difficult to address, because of the chronic lack of effective laboratory settings for experimentation. In this paper, we describe a system representative of the usual complexity found in cyber-physical systems, whose purpose is to address the needs for experimenting with building automation, with a focus on control systems and gamification. Designed with pragmatic concerns, this system presents a unique set of challenges and opportunities to research a new generation of software control systems, and supporting interfaces, that leverage the occupants’ behaviour.