Usability of a mobile, group communication prototype while rendezvousing

Mobile phones are increasingly being used collaboratively by social networks of users in spite of the fact that they are primarily designed to support single users and one-to-one communication. It is not well understood how services such as group SMS, SMS-based discussion lists and mobile instant messaging (IM) will be used by mobile groups in natural settings. Studying specific instances of common styles of in situ, group interaction may provide a way to see behavior patterns and typical interaction problems. We conducted a study of a mobile, group communication probe used during a rendezvousing activity in an urban environment. Usability problems relating to group usage, phone interface design and context were identified. Several major issues included: multitasking during message composition and reading; speed of text entry; excessive demand on visual attention; and ambiguity of intended recipients. We suggest that existing mobile device designs are overly-focused on individual users to the detriment of usability for mobile groups of users. We provide recommendations for the design of future mobile, group interfaces, used in similar situations to those explored here

Evaluating approaches to product design and sourcing decisions in multinational companies

Product design and sourcing decisions are among the most difficult and important of all decisions facing multinational manufacturing companies, yet associated decision support and evaluation systems tend to be myopic in nature. Design for manufacture and assembly techniques, for example, generally focuses on manufacturing capability and ignores capacity although both should be considered. Similarly, most modelling and evaluation tools available to examine the performance of various solution and improvement techniques have a narrower scope than desired. A unique collaboration, funded by the US National Science Foundation, between researchers in the USA and the UK currently addresses these problems. This paper describes a technique known as Design For the Existing Environment (DFEE) and an holistic evaluation system based on enterprise simulation that was used to demonstrate the business benefits of DFEE applied in a simple product development and manufacturing case study. A project that will extend these techniques to evaluate global product sourcing strategies is described along with the practical difficulties of building an enterprise simulation on the scale and detail required.

Feature LDA:a supervised topic model for automatic detection of web API documentations from the web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Software engineering for self-adaptive systems:a research roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. The paper is partitioned into four parts, one for each of the identified essential views of self-adaptation: modelling dimensions, requirements, engineering, and assurances. For each view, we present the state-of-the-art and the challenges that our community must address. This roadmap paper is a result of the Dagstuhl Seminar 08031 on "Software Engineering for Self-Adaptive Systems," which took place in January 2008. © 2009 Springer Berlin Heidelberg.

Using architecture models to support the generation and operation of component-based adaptive systems

Modelling architectural information is particularly important because of the acknowledged crucial role of software architecture in raising the level of abstraction during development. In the MDE area, the level of abstraction of models has frequently been related to low-level design concepts. However, model-driven techniques can be further exploited to model software artefacts that take into account the architecture of the system and its changes according to variations of the environment. In this paper, we propose model-driven techniques and dynamic variability as concepts useful for modelling the dynamic fluctuation of the environment and its impact on the architecture. Using the mappings from the models to implementation, generative techniques allow the (semi) automatic generation of artefacts making the process more efficient and promoting software reuse. The automatic generation of configurations and reconfigurations from models provides the basis for safer execution. The architectural perspective offered by the models shift focus away from implementation details to the whole view of the system and its runtime change promoting high-level analysis. © 2009 Springer Berlin Heidelberg.