Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thousands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most popular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called SwiftHand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is often computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly available tool for Android apps written in Java. Our experimental results show that we can achieve significantly better coverage than traditional random testing and L * -based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L * -based testing.

In practice, it is quite difficult to write correct multithreaded programs due to the potential for unintended and nondeterministic interference between parallel threads. A fundamental correctness property for such programs is atomicity-a block of code in a program is atomic if, for any parallel execution of the program, there is an execution with the same overall program behavior in which the block is executed serially.We propose semantic atomicity, a generalization of atomicity with respect to a programmer-defined notion of equivalent behavior. We propose an assertion framework in which a programmer can use bridge predicates to specify noninterference properties at the level of abstraction of their application. Further, we propose a novel algorithm for systematically testing atomicity specifications on parallel executions with a bounded number of interruptionsi.e. atomic blocks whose execution is interleaved with that of other threads. We further propose a set of sound heuristics and optional user annotations that increase the efficiency of checking atomicity specifications in the common case where the specifications hold.We have implemented our assertion framework for specifying and checking semantic atomicity for parallel Java programs, and we have written semantic atomicity specifications for a number of benchmarks. We found that using bridge predicates allowed us to specify the natural and intended atomic behavior of a wider range of programs than did previous approaches. Further, in checking our specifications, we found several previously unknown bugs, including in the widely-used java.util.concurrent library.