Online social networks are now common place in day-to-day lives. They are also increasingly used to drive social action initiatives, either led by government or communities themselves (e.g., SeeClickFix, LoveLewisham.org, mumsnet). However, such initiatives are mainly used for crowd sourcing community views or coordinating activities. With the changing global economic and political landscape, there is an ever pressing need to engage citizens on a large-scale, not only in consultations about systems that affect them, but also involve them directly in the design of these very systems. In this paper we present the UDesignIt platform that combines social media technologies with software engineering concepts to empower communities to discuss and extract high-level design features. It combines natural language processing, feature modelling and visual overlays in the form of “image clouds” to enable communities and software engineers alike to unlock the knowledge contained in the unstructured and unfiltered content of social media where people discuss social problems and their solutions. By automatically extracting key themes and presenting them in a structured and organised manner in near real-time, the approach drives a shift towards large-scale engagement of community stakeholders for system design.

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base code can lead to join points incorrectly falling in or out of the scope of pointcuts. In this paper, we present an automated approach that limits fragility problems by providing mechanical assistance in pointcut maintenance. The approach is based on harnessing arbitrarily deep structural commonalities between program elements corresponding to join points selected by a pointcut. The extracted patterns are then applied to later versions to offer suggestions of new join points that may require inclusion. To illustrate that the motivation behind our proposal is well founded, we first empirically establish that join points captured by a single pointcut typically portray a significant amount of unique structural commonality by analyzing patterns extracted from 23 AspectJ programs. Then, we demonstrate the usefulness of our technique by rejuvenating pointcuts in multiple versions of three of these programs. The results show that our parameterized heuristic algorithm was able to accurately and automatically infer the majority of new join points in subsequent software versions that were not captured by the original pointcuts.

The requirements and design level identification and representation of dynamic variability for adaptive systems is a challenging task. This requires time and effort to identify and model the relevant elements as well as the need to consider the large number of potentially possible system configurations. Typically, each individual variability dimension needs to identified and modelled by enumerating each possible alternative. The full set of requirements needs to be reviewed to extract all potential variability dimensions. Moreover, each possible configuration of an adaptive system needs to be validated before use. In this demonstration, we present a tool suite that is able to manage dynamic variability in adaptive systems and tame such system complexity. This tool suite is able to automatically identify dynamic variability attributes such as variability dimensions, context, adaptation rules, and soft/hard goals from requirements documents. It also supports modelling of these artefacts as well as their run-time verification and validation.

Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. We present an automated approach that limits fragility problems by providing mechanical assistance in pointcut maintenance. The approach is based on harnessing arbitrarily deep structural commonalities between program elements corresponding to join points selected by a pointcut. The extracted patterns are then applied to later versions to offer suggestions of new join points that may require inclusion. We demonstrate the usefulness of our technique by rejuvenating pointcuts in multiple versions of several open-source AspectJ programs. The results show that our parameterized heuristic algorithm was able to automatically infer new join points in subsequent versions with an average recall of 0.93. Moreover, these join points appeared, on average, in the top 4thpercentile of the suggestions, indicating that the results were precise.

One of the aims of aspect-oriented requirements engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. Composing concerns may help to reveal conflicting dependencies that need to be identified and resolved. However, detecting conflicts in a large set of textual aspect-oriented requirements is an error-prone and time-consuming task. This paper presents EA-analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method that has been effective at classifying text. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different real-life domains. We show that the tool achieves up to 92.97% accuracy when one of the case study documents is used as a training set and the other two as a validation set.

Abstract Flooding 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 fixed‐network 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. Copyright © 2007 John Wiley & Sons, Ltd.

Aspect-Oriented Requirements Engineering (AORE) aims at improving separation of concerns in the problem space by offering new ways of modularising requirements. Over recent years several AORE approaches have emerged by evolving contemporary requirements approaches such as viewpoints-, scenarios- and goal-based models. Due to the novelty of these techniques, there is a lack of systematic comparative studies analyzing the benefits and drawbacks they can offer to the requirements engineering practice. This paper presents a case study contrasting four eminent AORE approaches in terms of time effectiveness and accuracy of their produced outcome. We address challenges related to the heterogeneous definitions for AORE model concepts as well as the fact that they perform similar general requirements process activities in different ways. In order to address these challenges, we provide a mapping of the AORE approaches onto general RE activities and provide a common naming scheme. The case study results show that specification of aspect compositions in AORE presents an effort bottleneck that has to be carefully weighed against the added benefits of modularity and analysis of systemic properties offered by AORE. Consequently, our study provides an initial yet significant stepping stone towards improving the evaluation of AORE approaches and understanding their contribution to requirements engineering.

Current approaches to flood monitoring (e.g. in river valleys) involve statically deploying depth and ultrasoundbased flow sensors across flood-prone areas, and feeding the collected data off-site (e.g. using GSM) to grid-based

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 component-based Grid middleware platform 'GridKit', which offers inherent support for reconfiguration, embedded devices and heterogeneous network technologies, making it ideally suited to this application.

Thorough testing of distributed systems, particularly peer-to-peer systems can prove difficult due to the problems inherent in deploying, controlling and monitoring many nodes simultaneously. This problem will only increase as the scale of distributed systems continues to grow. This framework implements a test bed environment using a semi-centralized peer-to-peer network as a substrate for sharing resources made available from standard PCs. This framework automates the process of test-case deployment using a combination of reflection and aspect oriented programming. This allows 'point-and-click' publishing of software onto the test-bed. Our framework also provides a common monitoring, control and logging interface for all nodes running on the network. Together, these features greatly reduce deployment-time for real-world test scenarios. Automated insertion and removal of test code also ensures that the testing process does not compromise the correctness of the final system.