Applying and Evaluating Concern-Sensitive Design Heuristics

Abstract-Empirical studies have stressed that aspect-oriented decompositions can cause non-obvious flaws in the modularity of certain design concerns. Without proper design evaluation mechanisms, the identification of these flaws can become counter-productive and impractical. Nowadays, modularity assessment is mostly supported by metric-based heuristics rooted at conventional attributes, such as module cohesion and coupling. However, such conventional module-driven assessment cannot be tailored to the design concerns. This paper proposes and systematically evaluates a representative suite of concern-sensitive heuristic rules. The accuracy of the heuristics is assessed through their application to six systems. The analysis was based on the heuristics support for: (i) addressing the shortcomings of conventional metrics-based assessments, (ii) reducing the manifestation of false positives and false negatives, and (iii) finding the presence of design flaws relative to both classes and aspects. Resumo-Estudos experimentais recentes mostraram que decomposições orientadas a aspectos podem causar anomalias na modularidade do design de certos interesses e que tais anomalias muitas vezes não são óbvias. Sem mecanismos de avaliação de design apropriados, a identificação dessas anomalias pode se tornar contraproducente e impraticável. Atualmente, a avaliação da modularidade de design orientado a aspectos é na maioria das vezes apoiada por heurísticas baseadas em métricas que quantificam atributos convencionais, como coesão e acoplamento de módulos. No entanto, essa avaliação dirigida por atributos convencionais não leva em conta os interesses que guiam o design. Esse artigo propõe e avalia sistematicamente um conjunto de regras heurísticas sensíveis a interesses. A acurácia das heurísticas foi avaliada por meio de sua aplicação em seis diferentes sistemas. A análise se baseou na capacidade das heurísticas de: (i) tratar das limitações de abordagens de avaliação baseadas em métricas convencionais, (ii) detectar a presença de anomalias de design relacionadas a classes e aspectos, e (iii) reduzir a manifestação de falsos positivos e falsos negativos

The display of electronic commerce within virtual environments

In today’s competitive business environment, the majority of companies are expected to be represented on the Internet in the form of an electronic commerce site. In an effort to keep up with current business trends, certain aspects of interface design such as those related to navigation and perception may be overlooked. For instance, the manner in which a visitor to the site might perceive the information displayed or the ease with which they navigate through the site may not be taken into consideration. This paper reports on the evaluation of the electronic commerce sites of three different companies, focusing specifically on the human factors issues such as perception and navigation. Heuristic evaluation, the most popular method for investigating user interface design, is the technique employed to assess each of these sites. In light of the results from the analysis of the evaluation data, virtual environments are suggested as a way of improving the navigation and perception display constraints

The assessment of usability of electronic shopping: A heuristic evaluation

Today there are thousands of electronic shops accessible via the Web. Some provide user-friendly features whilst others seem not to consider usability factors at all. Yet, it is critical that the electronic shopping interface is user-friendly so as to help users to obtain their desired results. This study applied heuristic evaluation to examine the usability of current electronic shopping. In particular, it focused on four UK-based supermarkets offering electronic services: including ASDA, Iceland, Sainsbury, and Tesco. The evaluation consists of two stages: a free-flow inspection and a task-based inspection. The results indicate that the most significant and common usability problems have been found to lie within the areas of ‘User Control and Freedom’ and ‘Help and Documentation’. The findings of this study are applied to develop a set of usability guidelines to support the future design of effective interfaces for electronic shopping

Encouraging Privacy-Aware Smartphone App Installation: Finding out what the Technically-Adept Do

Smartphone apps can harvest very personal details from the phone with ease. This is a particular privacy concern. Unthinking installation of untrustworthy apps constitutes risky behaviour. This could be due to poor awareness or a lack of knowhow: knowledge of how to go about protecting privacy. It seems that Smartphone owners proceed with installation, ignoring any misgivings they might have, and thereby irretrievably sacrifice their privacy

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

The relationship between likelihood and fear of criminal victimisation: evaluating risk sensitivity as a mediating concept

Crime surveys typically ask respondents how ‘likely’ they think it is that they will become a crime victim in the future. The responses are interpreted here as ‘risk’ statements. An investigation of the risk literature shows the concept to be considerably more complex than at first imagined, but shows that individual risk predictions are largely based on interpretations far removed from rational considerations of likelihood based on recorded crime rates. Responses from three waves of a longitudinal crime survey conducted in Trinidad are examined in this light. It is concluded that fear of criminal victimization might best be considered as differential sensitivity to predicted risk.</p

Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks