A Resource-Aware Component Model for Embedded Systems
Sammanfattning: Embedded systems are microprocessor-based systems that cover a large range of computer systems from ultra small computer-based devices to large systems monitoring and controlling complex processes. The particular constraints that must be met by embedded systems, such as timeliness, resource-use efficiency, short time-to-market and low cost, coupled with the increasing complexity of embedded system software, demand technologies and processes that will tackle these issues. An attractive approach to manage the software complexity, increase productivity, reduce time to market and decrease development costs, lies in the adoption of the component based software engineering (CBSE) paradigm. The specific characteristics of embedded systems lead to important design issues that need to be addressed by a component model. Consequently, a component model for development of embedded systems needs to systematically address extra-functional system properties. The component model should support predictable system development and as such guarantee absence or presence of certain properties. Formal methods can be a suitable solution to guarantee the correctness and reliability of software systems. Following the CBSE spirit, in this thesis we introduce the ProCom component model for development of distributed embedded systems. ProCom is structured in two layers, in order to support both a high-level view of loosely coupled subsystems encapsulating complex functionality, and a low-level view of control loops with restricted functionality. These layers differ from each other in terms of execution model, communication style, synchronization etc., but also in kind of analysis which are suitable. To describe the internal behavior of a component, in a structured way, in this thesis we propose REsource Model for Embedded Systems (REMES) that describes both functional and extra-functional behavior of interacting embedded components. We also formalize the resource-wise properties of interest and show how to analyze such behavioral models against them.
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