Many modern user-intensive applications, such as Web applications, must satisfy the interaction requirements of thousands if not millions of users, which can be hardly fully understood at design time. Designing applications that meet user behaviors, by efficiently supporting the prevalent navigation patterns, and evolving with them requires new approaches that go beyond classic software engineering solutions. We present a novel approach that automates the acquisition of user-interaction requirements in an incremental and reflective way. Our solution builds upon inferring a set of probabilistic Markov models of the users' navigational behaviors, dynamically extracted from the interaction history given in the form of a log file. We annotate and analyze the inferred models to verify quantitative properties by means of probabilistic model checking. The paper investigates the advantages of the approach referring to a Web application currently in use.

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.

Applications running on mobile devices are heavily context-aware and adaptive, leading to new analysis and testing challenges as streams of context values drive these applications to undesired configurations that are not easily exposed by existing validation techniques. We address this challenge by employing a finite-state model of adaptive behavior to enable the detection of faults caused by (1) erroneous adaptation logic, and (2) asynchronous updating of context information, which leads 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 that we detect automatically by analyzing the system's adaptation model. We illustrate our approach on a simple but realistic application in which a cellphone's configuration profile is changed automatically based on the user's location, speed and surrounding environment. © 2008 ACM.