Model Based Development of Quality-Aware Software Services

Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration

Developing and Evaluating Cognitive Architectures with Behavioural Tests

http://www.aaai.org/Press/Reports/Workshops/ws-07-04.phpWe present a methodology for developing and evaluating cognitive architectures based on behavioural tests and suitable optimisation algorithms. Behavioural tests are used to clarify those aspects of an architecture's implementation which are critical to that theory. By fitting the performance of the architecture to observed behaviour, values for the architecture's parameters can be automatically obtained, and information can be derived about how components of the architecture relate to performance. Finally, with an appropriate optimisation algorithm, different cognitive architectures can be evaluated, and their performances compared on multiple tasks.Peer reviewe

Mirroring or misting: On the role of product architecture, product complexity, and the rate of product component change

This paper contributes to the literature on the within-firm and across-firm mirroring hypothesis – the assumed architectural mapping between firms’ strategic choices of product architecture and firm architecture, and between firms’ architectural choices and the industry structures that emerge. Empirical evidence is both limited and mixed and there is evidently a need for a more nuanced theory that embeds not only whether the mirroring hypothesis holds, but under what product architecture and component-level conditions it may or may not hold. We invoke an industrial economics perspective to develop a stylised product architecture typology and hypothesise how the combined effects of product architecture type, product complexity and the rate of product component change may be associated with phases of mirroring or misting. Our framework helps to reconcile much existing mixed evidence and provides the foundation for further empirical research

Using genetic algorithms to generate test sequences for complex timed systems

The generation of test data for state based specifications is a computationally expensive process. This problem is magnified if we consider that time con- straints have to be taken into account to govern the transitions of the studied system. The main goal of this paper is to introduce a complete methodology, sup- ported by tools, that addresses this issue by represent- ing the test data generation problem as an optimisa- tion problem. We use heuristics to generate test cases. In order to assess the suitability of our approach we consider two different case studies: a communication protocol and the scientific application BIPS3D. We give details concerning how the test case generation problem can be presented as a search problem and automated. Genetic algorithms (GAs) and random search are used to generate test data and evaluate the approach. GAs outperform random search and seem to scale well as the problem size increases. It is worth to mention that we use a very simple fitness function that can be eas- ily adapted to be used with other evolutionary search techniques