Clafer: Lightweight Modeling of Structure, Behaviour, and Variability

Embedded software is growing fast in size and complexity, leading to intimate mixture of complex architectures and complex control. Consequently, software specification requires modeling both structures and behaviour of systems. Unfortunately, existing languages do not integrate these aspects well, usually prioritizing one of them. It is common to develop a separate language for each of these facets. In this paper, we contribute Clafer: a small language that attempts to tackle this challenge. It combines rich structural modeling with state of the art behavioural formalisms. We are not aware of any other modeling language that seamlessly combines these facets common to system and software modeling. We show how Clafer, in a single unified syntax and semantics, allows capturing feature models (variability), component models, discrete control models (automata) and variability encompassing all these aspects. The language is built on top of first order logic with quantifiers over basic entities (for modeling structures) combined with linear temporal logic (for modeling behaviour). On top of this semantic foundation we build a simple but expressive syntax, enriched with carefully selected syntactic expansions that cover hierarchical modeling, associations, automata, scenarios, and Dwyer's property patterns. We evaluate Clafer using a power window case study, and comparing it against other notations that substantially overlap with its scope (SysML, AADL, Temporal OCL and Live Sequence Charts), discussing benefits and perils of using a single notation for the purpose

A component-based product line architecture for workflow management systems

This paper presents a component-based product line for workflow management systems. The process followed to design the product line was based on the Catalysis method. Extensions were made to represent variability across the process. The domain of workflow management systems has been shown to be appropriate to the application of the product line approach as there are a standard architecture and models established by a regulatory board, the Workflow Management Coalition. In addition, there is a demand for similar workflow management systems but with some different features. The product line architecture was evaluated with Rapide simulation tools. The evaluation was based on selected scenarios, thus, avoiding implementation issues. The strategy that has been used to populate the architecture and experiment with the product line is shown. In particular, the design of the workflow execution manager component is described

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Sampling soil organic carbon to detect change over time

This research describes a generic monitoring design that could be widely applied to detect temporal changes in soil organic carbon stocks (SOC) across a carbon estimation area (CEA) with no prior knowledge of the spatial or temporal variance of SOC within the CEA. The report includes information on: Bases for designing SOC stock sampling for detecting change Monitoring SOC change to verify the effects of land use or management practicesStatistical rationale for monitoring SOC changeQuality measure and constraints for monitoring SOC changeDesign-based optimisation of sample sizesModel-based optimisation of sample sizesHypothesis testingStatistical model for monitoring SOC changeUsing available data and its variability to guide initial sampling designUncertainty in outcomes of monitoring designsSummary and conclusions