Gabor Karsai

Smart Dust: communicating with a cubic-millimeter computer

W hat do Rational Rose, Simulink, and LabVIEW have in common? At first, these tools seem very different. Rational Rose (http://www.rational.com) is a visual modeling tool, Simulink (http:// www.mathworks.com) is a hierarchical block-diagram design and simulation tool, and LabVIEW (http:// www.ni.com) is a graphical programming development environment. Despite the different terminology, these three tools share a common underlying theme: Each is an integrated set of modeling, model analysis, simulation, and code-generation tools that help design and implement computer-based systems (CBSs) in a specific, well-defined engineering field. These tools and other popular domain-specific integrated development environments can help capture specifications in the form of domain models. They also support the design process by automating analysis and simulating essential system behavior. In addition, they can automatically generate, configure, and integrate target application components. These environments translate the verified design—expressed in a domainspecific, primarily graphical modeling formalism—into a variety of artifacts that constitute a CBS implementation. These artifacts can include glue code, database schema, and configuration tables. These tools use domain-specific modeling languages that allow developers to represent essential design views and to both formally express and automatically enforce integrity constraints. These tools also support model composition that is synergistic with the design process in the particular engineering domain. Other benefits include having integrated models as opposed to relying merely on source code. In addition, the common input—that is, the shared design model—guarantees the consistency of different analysis results as long as all of the applied generators are correct. While the industry understands the welldocumented benefits of domain-specific, integrated modeling, analysis, and application-generation environments, their high cost represents a significant block to wide acceptance and application. Consequently, these tools are available only for domains with large markets in which high volume offsets the substantial initial investment cost. For CBSs in smaller, specialized domains, or even for single projects, the industry needs technology that can help rapidly and efficiently compose these environments from reusable components.Domain-specific integrated development environments can help capture specifications in the form of domain models. These tools support the design process by automating analysis and simulating essential system behavior. In addition, they can automatically generate, configure, and integrate target application components. The high cost of developing domain-specific, integrated modeling, analysis, and application-generation environments prevents their penetration into narrower engineering fields that have limited user bases. Model-integrated computing (MIC), an approach to model-based engineering that helps compose domain-specific design environments rapidly and cost effectively, is particularly relevant for specialized computer-based systems domains-perhaps even single projects. The authors describe how MIC provides a way to compose such environments cost effectively and rapidly by using a metalevel architecture to specify the domain-specific modeling language and integrity constraints. They also discuss the toolset that implements MIC and describe a practical application in which using the technology in a tool environment for the process industry led to significant reductions in development and maintenance costs.

Model-integrated computing

Computers now control many critical systems in our lives, from the brakes on our cars to the avionics control systems on planes. Such computers wed physical systems to software, tightly integrating the two and generating complex component interactions unknown in earlier systems. Thus, it is imperative that we construct software and its associated physical system so they can evolve together. The paper discusses one approach that accomplishes this called model-integrated computing. This works by extending the scope and use of models. It starts by defining the computational processes that a system must perform and develops models that become the backbone for the development of computer-based systems. In this approach, integrated, multiple-view models capture information relevant to the system under design. The paper considers the Multigraph Architecture framework for model-integrated computing developed at Vanderbilts Measurement and Computing Systems Laboratory.

Model-integrated development of embedded software

The paper describes a model-integrated approach for embedded software development that is based on domain-specific, multiple-view models used in all phases of the development process. Models explicitly represent the embedded software and the environment it operates in, and capture the requirements and the design of the application, simultaneously. Models are descriptive , in the sense that they allow the formal analysis, verification, and validation of the embedded system at design time. Models are also generative, in the sense that they carry enough information for automatically generating embedded systems using the techniques of program generators. Because of the widely varying nature of embedded systems, a single modeling language may not be suitable for all domains; thus, modeling languages are often domain-specific. To decrease the cost of defining and integrating domain-specific modeling languages and corresponding analysis and synthesis tools, the model-integrated approach is applied in a metamodeling architecture, where formal models of domain-specific modeling languages-called metamodels-play a key role in customizing and connecting components of tool chains. This paper discusses the principles and techniques of model-integrated embedded software development in detail, as well as the capabilities of the tools supporting the process. Examples in terms of real systems will be given that illustrate how the model-integrated approach addresses the physical nature, the assurance issues, and the dynamic structure of embedded software.

Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Toward a Science of Cyber–Physical System Integration

System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.

Developing applications using model-driven design environments

Historically, software development methodologies have focused more on improving tools for system development than on developing tools that assist with system composition and integration. Component-based middleware like Enterprise Java-Beans (EJB), Microsoft .NET, and the CORBA Component Model (CCM) have helped improve software reusability through component abstraction. However, as developers have adopted these commercial off-the-shelf technologies, a wide gap has emerged between the availability and sophistication of standard software development tools like compilers and debuggers, and the tools that developers use to compose, analyze, and test a complete system or system of systems. As a result, developers continue to accomplish system integration using ad hoc methods without the support of automated tools. Model-driven development is an emerging paradigm that solves numerous problems associated with the composition and integration of large-scale systems while leveraging advances in software development technologies such as component-based middleware. MDD elevates software development to a higher level of abstraction than is possible with third-generation programming languages.

Artificial neural networks applied to arc welding process modeling and control

The authors explain some basic concepts relating to neural networks and discuss how they can be used to model weld bead geometry in terms of the parameters of the equipment selected to produce the weld. Approaches to utilization of neural networks in process control are discussed as well. The need for modeling transient as well as static characteristics of physical systems for closed-loop control is pointed out, and an approach for achieving this is presented. The performance of neural networks for modeling is presented and evaluated using actual welding data. It is concluded that the accuracy of neural network modeling is fully comparable to the accuracy achieved by more traditional modeling schemes.<<ETX>>

Semantic Translation of Simulink/Stateflow Models to Hybrid Automata Using Graph Transformations

Embedded systems are often modeled using Matlabs Simulink and Stateflow (MSS), to simulate plant and controller behavior but these models lack support for formal verification. On the other hand verification techniques and tools do exist for models based on the notion of Hybrid Automata (HA) but there are no tools that can convert Simulink/Stateflow models into their semantically equivalent Hybrid Automata models. This paper describes a translation algorithm that converts a well-defined subset of the MSS modeling language into an equivalent hybrid automata. The translation has been specified and implemented using a metamodel-based graph transformation tool. The translation process allows semantic interoperability between the industry-standard MSS tools and the new verification tools developed in the research community.

A domain-specific visual language for domain model evolution

Abstract Domain-specific visual languages (DSVLs) are concise and useful tools that allow the rapid development of the behavior and/or structure of applications in well-defined domains. These languages are typically developed specifically for a domain, and have a strong cohesion to the domain concepts, which often appear as primitives in the language. The strong cohesion between DSVL language primitives and the domain is a benefit for development by domain experts, but can be a drawback when the domain evolves—even when that evolution appears to be insignificant. This paper presents a domain-specific visual language developed expressly for the evolution of domain-specific visual languages, and uses concepts from graph rewriting to specify and carry out the transformation of the models built using the original DSVL.

Constraint-Based Design-Space Exploration and Model Synthesis

An important bottleneck in model-based design of embedded systems is the cost of constructing models. This cost can be significantly decreased by increasing the reuse of existing model components in the design process. This paper describes a tool suite, which has been developed for component-based model synthesis. The DESERT tool suite can be interfaced to existing modeling and analysis environments and can be inserted in various, domain specific design flows. The modeling component of DESERT supports the modeling of design spaces and the automated search for designs that meet structural requirements. DESERT has been introduced in automotive applications and proved to be useful in increasing design productivity.