Context-aware Adaptive Applications Research Articles

Semantic analysis and verification of context-driven adaptive applications in intelligent environments

With the advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), assistive applications and smart environments can tap into a growing amount of context information to learn about their users, their surroundings and typical behavior. This information is useful to adapt intelligently, autonomously and non-intrusively to a myriad of circumstances. However, trustworthiness and reliable adaptation to changes in line with user expectations—especially to situations that the developers did not anticipate—remain key concerns. Understanding the impact of changes from a developer (design time) and system (runtime) perspective, and ensuring that no undesired side effects take place are two non-trivial research challenges to increase the adoption of such applications. Given the limited tool support for anticipating change at design time and runtime, we present our change impact analysis (CIA) methodology—found in the formal semantic modeling of intelligent environments and rule-based application behavior—to contribute to the development and deployment of reliable context-aware adaptive applications. We validate our contributions on non-trivial smart home and office scenarios, and demonstrate how our framework helps increase trust in intelligent environment applications by anticipating change implications upfront at design time and by minimizing the occurrence of undesired side effects at runtime.

AFChecker: Effective model checking for context-aware adaptive applications

Context-aware adaptive applications continually sense and adapt to their changing environments. A large body of such applications relies on user-configured adaptation rules to customize their behavior. We call them rule-based context-aware applications (or RBAs for short). Due to the complexity required for adequately modeling environmental dynamics, adaptation faults are common in these RBAs. One promising approach to detecting such faults is to build a state transition model for an RBA, and exhaustively explore the model's state space. However, it can suffer from numerous false positives. For example, 78.6% of 784 reported faults for one popular RBA – PhoneAdapter, turn out to be false in a real deployment. In this paper, we address this false positive problem by inferring a domain model and an environment model for an RBA. The two models capture the hidden features inside user-configured adaptation rules as well as the RBA's running environment. We formulate these features as deterministic constraints and probabilistic constraints to prune false positives and effectively prioritize remaining faults. Our experiments on two real RBAs report that this approach successfully removes 46.5% of false positives and ranks 86.2% of true positives to the top of the fault list.

Context-Aware Adaptive Applications: Fault Patterns and Their Automated Identification

Applications running on mobile devices are intensely context-aware and adaptive. Streams of context values continuously drive these applications, making them very powerful but, at the same time, susceptible to undesired configurations. Such configurations are not easily exposed by existing validation techniques, thereby leading to new analysis and testing challenges. In this paper, we address some of these challenges by defining and applying a new model of adaptive behavior called an Adaptation Finite-State Machine (A-FSM) to enable the detection of faults caused by both erroneous adaptation logic and asynchronous updating of context information, with the latter leading to inconsistencies between the external physical context and its internal representation within an application. We identify a number of adaptation fault patterns, each describing a class of faulty behaviors. Finally, we describe three classes of algorithms to detect such faults automatically via analysis of the A-FSM. We evaluate our approach and the trade-offs between the classes of algorithms on a set of synthetically generated Context-Aware Adaptive Applications (CAAAs) and on a simple but realistic application in which a cell phone's configuration profile changes automatically as a result of changes to the user's location, speed, and surrounding environment. Our evaluation describes the faults our algorithms are able to detect and compares the algorithms in terms of their performance and storage requirements.

Multi-layer faults in the architectures of mobile, context-aware adaptive applications

Modern hand-held devices are equipped with multiple context sensors exploited by increasingly sophisticated software applications, called Context-Aware Adaptive Applications (CAAAs), that adapt automatically to changes in the surrounding environment, such as by responding to the location and speed of the user. The architecture of CAAAs is typically layered and incorporates a context-awareness component to support processing of context values and triggering of adaptive changes. While this layered architecture is very natural for the design and implementation of CAAAs, it exhibits new kinds of failures that arise as a result of faults that are specific to the choice of technology for specific layers. In this paper we investigate the occurrence of such faults and failures that manifest across architectural layers, and we describe samples of such failures in four CAAAs.