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.