Scenario based requirements specifications are the industry norm for defining communicating systems. These scenarios are often captured in the form of UML/MSC sequence diagrams. Errors are often introduced at this stage of the development process, which are costly to resolve if they are not detected early. This paper is concerned with the automatic detection and resolution of semantic errors that can occur in such scenarios.The paper discusses a semantic interpretation of scenario-based requirements and various types of defects (or pathologies) that can be detected. The paper defines the semantics and defects within a partial order theoretic framework. We introduce a UML 2.0 profile that captures various domain specific communication semantics, which can be used to determine the relevance of detected pathologies when different underlying implementation assumptions are made. The paper also discusses how to automatically resolve pathologies by using this profile to adapt the communication architecture in the requirements model.

Algorithmic skeletons are abstractions from common patterns of parallel activity which offer a high degree of reusability for developers of parallel algorithms. Their close association with higher order functions (HOFs) makes functional languages, with their strong transformational properties, excellent vehicles for skeleton-based parallel program development. However, using HOFs in this way raises substantial problems of identification of useful HOFs within a given application and of resource allocation on target architectures. We present the design and implementation of a parallelising compiler for Standard ML which exploits parallelism in the familiar $map$ and $fold$ HOFs through skeletons for processor farms and processor trees, respectively. The compiler extracts parallelism automatically and is target architecture independant. HOF execution within a functional language can be nested in the sense that one HOF may be passed and evaluated during the execution of another HOF. We are able to exploit this by nesting our parallel skeletons in a processor topology which matches the structure of the Standard ML source. However, where HOF arguments result from partially applied functions, free variable bindings must be identified and communicated through the corresponding skeleton hierarchy to where those arguments are actually applied. We describe the analysis leading from input Standard ML through HOF instantiation and backend compilation to an executable parallel program. We also present an overview of the runtime system and the execution model. Finally, we give parallel performance figures for several example programs, of varying computational loads, on the Linux-based Beowulf, IBM SP/2, Fujitsu AP3000 and Sun StarCat 15000 MIMD parallel machines. These demonstrate good cross-platform consistency of parallel code behaviour.