The Generic Modeling Environment (GME) is a configurable toolset that supports the easy creation of domain-specific modeling and program synthesis environments. The primarily graphical, domain-specific models can represent the application and its environment including hardware resources, and their relationship. The models are then used to automatically synthesize the application and/or generate inputs to COTS analysis tools. In addition to traditional signal processing problems, we have applied this approach to tool integration and structurally adaptive systems among other domains. This paper describes the GME toolset and compares it to other similar approaches. A case study is also presented that illustrates the core concepts through an example. 1.

Proceedings of the IEEE January 2003 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. The paper will discuss 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.

Modern embedded computing systems tend to be heterogeneous in the sense of being composed of subsystems with very different characteristics, which communicate and interact in a variety of ways---synchronous or asynchronous, buffered or unbuffered, etc. Obviously, when designing such systems, a modeling language needs to reflect this heterogeneity. Today's modeling environments usually offer a variant of what we call amorphous heterogeneity to address this problem. This paper argues that modeling systems in this manner leads to unexpected and hard-to-analyze interactions between the communication mechanisms and proposes a more structured approach to heterogeneity, called hierarchical heterogeneity to solve this problem. It proposes a model structure and semantic framework that support this form of heterogeneity, and discusses the issues arising from heterogeneous component interaction and the desire for component reuse. It introduces the notion of domain polymorphism as a way to address these issues.

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this paper presents an overview of component middleware and Model-Integrated Computing and then describes how combining the best elements of these two technologies can address the key challenges associated with developing enterprise applications

Abstract. Model-Integrated Computing (MIC) is an approach to Model-Driven Architecture (MDA), which has been developed primarily for embedded systems. MIC places strong emphasis on the use of domain-specific modeling languages (DSML-s) and model transformations. A metamodeling process facilitated by the Generic Modeling Environment (GME) tool suite enables the rapid and inexpensive development of DSML-s. However, the specification of semantics for DSML-s is still a hard problem. In order to simplify the DSML semantics, this paper discusses semantic anchoring, which is based on the transformational specification of semantics. Using a mathematical model, Abstract State Machine (ASM), as a common semantic framework, we have developed formal operational semantics for a set of basic models of computations, called semantic units. Semantic anchoring of DSML-s means the specification of model transformations between DSML-s (or aspects of complex DSML-s) and selected semantic units. The paper describes the semantic anchoring process using the meta-programmable MIC tool suite. 1

We describe an end-to-end tool-chain for modelbased design and analysis of component-based embedded real-time software. All aspects of an embedded real-time system are captured in domain-specific models, including software components and architecture, timing and resource constraints, processes and threads, execution platforms, etc. We focus on the AIRES tool, which performs various static analysis tasks based on the models, including system-level dependency analysis, execution rate assignment to component ports, real-time and schedulability analysis, and automated allocation of components to processors. By capturing all relevant information explicitly in the models at the design-level, and performing analysis that provide insight into nonfunctional aspects of the system, we can raise the level of abstraction for the designer, and facilitate rapid system prototyping. 1

Over 90 percent of all microprocessors are now used for real-time and embedded applications. Since the behavior of these applications is often constrained by the physical world, it is important to devise higher-level programming languages and middleware that robustly and productively enforce real-time constraints, as well as meeting conventional functional requirements. This paper provides two contributions to the study of programming languages and middleware for real-time and embedded applications. We first present how we are applying generative programming techniques to develop jRate, which is an open-source ahead-of-time-compiled implementation of the Real-time Specification for Java (RTSJ). The goal of jRate is to provide developers the ability to generate RTSJ implementations that are customized for their needs. We then show performance results of jRate that illustrate how well it performs compared to the TimeSys RTSJ Reference Implementation (RI).

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. There are considerable challenges, particularly because the physical components of such systems introduce safety and reliability requirements qualitatively different from those in generalpurpose computing. Moreover, physical components are qualitatively different from object-oriented software components. Standard abstractions based on method calls and threads do not work. This paper examines the challenges in designing such systems, and in particular raises the question of whether today’s computing and networking technologies provide an adequate foundation for CPS. It concludes that it will not be sufficient to improve design processes, raise the level of abstraction, or verify (formally or otherwise) designs that are built on today’s abstractions. To realize the full potential of CPS, we will have to rebuild computing and networking abstractions. These abstractions will have to embrace physical dynamics and computation in a unified way. 1