Pay-As-You-Go Software Artifacts Managemen

One of the major challenges in software engineering research is to manage software artifacts effectively. However, software artifacts are often changed during software development, the full, one-time integration technique is not feasible to manage such heterogeneity and evolving data. In this paper, we concern about the application of dataspace techniques, which emphasize the idea of pay-as-you-go data management, to software artifacts management. To this end, we present a loosely structured data model based on the current dataspace models to describe software artifacts, and a strategy to query this model. We also present how to gradually add semantics to query processing for improving the precision and recall of query results. Furthermore, the validity of our work is proved by experiment. Finally, the differences between our work and traditional work are discussed

AVALIAÇÃO DA CONSISTÊNCIA EM AMBIENTES INFORMACIONAIS DIGITAIS NO CONTEXTO DE ECOLOGIAS INFORMACIONAIS COMPLEXAS: proposta de checklist

A Arquitetura da Informação Pervasiva enfatiza o projeto e a avaliação de ecologias informacionais complexas. Uma de suas heurísticas é a Consistência que, quando aplicada, proporciona diálogo lógico entre os diferentes ambientes informacionais digitais pertencentes a uma ecologia informacional complexa, no que diz respeito à organização, à rotulagem, à representação, à navegação e à busca e recuperação da informação disponível. A partir dessas premissas, objetivou-se propor um checklist para avaliação da consistência de ecologias informacionais complexas, a partir de recomendações advindas da literatura científica, com utilização de pesquisa bibliográfica

Context-driven methodologies for context-aware and adaptive systems

Applications which are both context-aware and adapting, enhance users’ experience by anticipating their need in relation with their environment and adapt their behavior according to environmental changes. Being by definition both context-aware and adaptive these applications suffer both from faults related to their context-awareness and to their adaptive nature plus from a novel variety of faults originated by the combination of the two. This research work analyzes, classifies, detects, and reports faults belonging to this novel class aiming to improve the robustness of these Context-Aware Adaptive Applications (CAAAs). To better understand the peculiar dynamics driving the CAAAs adaptation mechanism a general high-level architectural model has been designed. This architectural model clearly depicts the stream of information coming from sensors and being computed all the way to the adaptation mechanism. The model identifies a stack of common components representing increasing abstractions of the context and their general interconnections. Known faults involving context data can be re-examined according to this architecture and can be classified in terms of the component in which they are happening and in terms of their abstraction from the environment. Resulting from this classification is a CAAA-oriented fault taxonomy. Our architectural model also underlines that there is a common evolutionary path for CAAAs and shows the importance of the adaptation logic. Indeed most of the adaptation failures are caused by invalid interpretations of the context by the adaptation logic. To prevent such faults we defined a model, the Adaptation Finite-State Machine (A-FSM), describing how the application adapts in response to changes in the context. The A-FSM model is a powerful instrument which allows developers to focus in those context-aware and adaptive aspects in which faults reside. In this model we have identified a set of patterns of faults representing the most common faults in this application domain. Such faults are represented as violation of given properties in the A-FSM. We have created four techniques to detect such faults. Our proposed algorithms are based on three different technologies: enumerative, symbolic and goal planning. Such techniques compensate each other. We have evaluated them by comparing them to each other using both crafted models and models extracted from existing commercial and free applications. In the evaluation we observe the validity, the readability of the reported faults, the scalability and their behavior in limited memory environments. We conclude this Thesis by suggesting possible extensions

Incremental Consistency Checking for Pervasive Context

Applications in pervasive computing are typically required to interact seamlessly with their changing environments. To provide users with smart computational services, these applications must be aware of incessant context changes in their environments and adjust their behaviors accordingly. As these environments are highly dynamic and noisy, context changes thus acquired could be obsolete, corrupted or inaccurate. This gives rise to the problem of context inconsistency, which must be timely detected in order to prevent applications from behaving anomalously. In this paper, we propose a formal model of incremental consistency checking for pervasive contexts. Based on this model, we further propose an efficient checking algorithm to detect inconsistent contexts. The performance of the algorithm and its advantages over conventional checking techniques are evaluated experimentally using Cabot middleware

Incremental consistency checking for pervasive context

Applications in pervasive computing are typically required to interact seamlessly with their changing environments. To provide users with smart computational services, these applications must be aware of incessant context changes in their environments and adjust their behaviors accordingly. As these environments are highly dynamic and noisy, context changes thus acquired could be obsolete, corrupted or inaccurate. This gives rise to the problem of context inconsistency, which must be timely detected in order to prevent applications from behaving anomalously. In this paper, we propose a formal model of incremental consistency checking for pervasive contexts. Based on this model, we further propose an efficient checking algorithm to detect inconsistent contexts. The performance of the algorithm and its advantages over conventional checking techniques are evaluated experimentally using Cabot middleware