Although one of the main promises of aspect-oriented (AO) programming techniques is to promote better software changeability than objectoriented (OO) techniques, there is no empirical evidence on their efficacy to prolong design stability in realistic development scenarios. For instance, no investigation has been performed on the effectiveness of AO decompositions to sustain overall system modularity and minimize manifestation of ripple-effects in the presence of heterogeneous changes. This paper reports a quantitative case study that evolves a real-life application to assess various facets of design stability of OO and AO implementations. Our evaluation focused upon a number of system changes that are typically performed during software maintenance tasks. They ranged from successive re-factorings to more broadly-scoped software increments relative to both crosscutting and non-crosscutting concerns. The study included an analysis of the application in terms of modularity, change propagation, concern interaction, identification of ripple-effects and adherence to well-known design principles.

SUMMARYFlooding is a growing problem, which affects more than 10% of the U.K. population. The cost of damage caused by flooding correlates closely with the warning time given before a flood event, making flood monitoring and prediction critical to minimizing the cost of flood damage. This paper describes a wireless sensor network (WSN) for flood warning, which is capable of not only integrating with remote fixednetwork grids for computationally intensive flood modelling purposes but also performing on-site grid computation. This functionality is supported by the reflective and component-based GridKit middleware, which provides support for both WSN and grid application domains.

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The cost of damage caused by flooding is directly related to the warning-time given before a flood occurs. Therefore, improving the coverage, accuracy and reliability of flood prediction systems is of great importance. This paper proposes a novel Grid-based approach to supporting flood prediction through the use of embedded sensor nodes equipped with wireless networking technology. These nodes implement a light-weight Grid capable of collecting and transmitting data gathered by flood sensors for off-site analysis. Additionally, the nodes are capable of performing basic on-site analysis that is used to inform system behavior. The proposed system utilizes Lancaster's next-generation componentbased Grid middleware platform 'GridKit', which offers inherent support for reconfiguration, embedded devices and heterogeneous network technologies, making it ideally suited to this application.

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