Context-driven methodologies for context-aware and adaptive systems

摘要

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