Publications

The development of cyber-physical systems (CPSs) with mechanical, electrical and software components requires a multi-disciplinary approach. Moreover, the use of models is important to support trade-offs and design decisions early in the development process. Since the different engineering disciplines use different modelling languages and tools, this calls for a co-simulation framework for discrete and continuous models.

This article studies the scheduling of real-time streaming applications on multiprocessor systems-on-chips with predictable memory hierarchy. An iteration-based task-FIFO co-scheduling framework is proposed for this problem. We obtain FIFO size distributions using Pareto space searching, based on which the task-toprocessor mapping is obtained with the potential FIFO allocation being taken into account; then, the FIFOto-memory allocation is optimized to minimize the total memory access cost; finally, a self-timed throughput analysis method that considers memory and direct memory access controller contention is utilized to analyze the throughput.

Multi-processor systems-on-chips are widely adopted in implementing modern streaming applications to satisfy the ever increasing computation requirements. To take advantage of this kind of platform, it is necessary to map tasks of the application properly to different processors, so as to fully exploit the inherent task-level parallelism and satisfy the stringent timing requirements.

A domain specific language (DSL) focuses on the essential concepts in a specific problem domain, and abstracts from low-level implementation details. The development of DSLs usually centers around the metamodel,grammar and code generator, possibly extended with transformations to analysis models. Typically, little attention is given to the formal semantics of the language, whereas this is essential for reasoning about DSL models, and for assessing the correctness of the generated code and analysis models.

Software maintenance consumes an increasing proportion of industrial software engineering budgets. Over time the technical debt grows, until it becomes unavoidable to rejuvenate the legacy software to a new design, while preserving the valuable domain logic. In this paper, we explore the feasibility of a model-based rejuvenation approach for use in an industrial context.

A domain specific language (DSL) abstracts from implementation details and is aligned with the way domain experts reason about a software component. The development of DSLs is usually centered around a grammar and transformations that generate implementation code or analysis models. The semantics of the language is often defined implicitly and in terms of a transformation to implementation code.

Many companies struggle with large amounts of legacy software that is difficult to maintain and to extend. Refactoring legacy code typically requires large efforts and introduces serious risks because often crucial business assets are hidden in legacy components. We investigate the support of formal techniques for the rejuvenation of legacy embedded software, concentrating on control components.

Two Domain Specific Languages (DSLs) have been developed to improve the development of a power control component of interventional X-ray systems of Philips. Configuration files and test cases are generated from instances of these DSLs. To increase the confidence in these instances and the generators, formal models have been generated to analyse DSL instances and to crosscheck the results of the generators.

Today’s manufacturing systems are typically complex cyber-physical systems where the physical and control aspects interact with the scheduling decisions. Optimizing such facilities requires ordering jobs and configuring the manufacturing system for each job. This optimization problem can be described as a Multi-Objective Generalized TSP where conflicting objectives lead to a trade-off space.