Abstract-Touchscreen-based devices such as smartphones and tablets are gaining popularity, but their rich input capabilities pose new development and testing complications. To alleviate this problem, we present an approach and tool named RERAN that permits record-and-replay for the Android smartphone platform. Existing GUI-level record-and-replay approaches are inadequate due to the expressiveness of the smartphone domain, in which applications support sophisticated GUI gestures, depend on inputs from a variety of sensors on the device, and have precise timing requirements among the various input events. We address these challenges by directly capturing the low-level event stream on the phone, which includes both GUI events and sensor events, and replaying it with microsecond accuracy. Moreover, RERAN does not require access to app source code, perform any app rewriting, or perform any modifications to the virtual machine or Android platform. We demonstrate RERAN's applicability in a variety of scenarios, including (a) replaying 86 out of the Top-100 Android apps on Google Play; (b) reproducing bugs in popular apps, e.g., Firefox, Facebook, Quickoffice; and (c) fast-forwarding executions. We believe that our versatile approach can help both Android developers and researchers.

Systematic exploration of Android apps is an enabler for a variety of app analysis and testing tasks. Performing the exploration while apps run on actual phones is essential for exploring the full range of app capabilities. However, exploring real-world apps on real phones is challenging due to non-determinism, non-standard control flow, scalability and overhead constraints. Relying on end-users to conduct the exploration might not be very effective: we performed a 7-user study on popular Android apps, and found that the combined 7-user coverage was 30.08% of the app screens and 6.46% of the app methods. Prior approaches for automated exploration of Android apps have run apps in an emulator or focused on small apps whose source code was available. To address these problems, we present A 3 E, an approach and tool that allows substantial Android apps to be explored systematically while running on actual phones, yet without requiring access to the app's source code. The key insight of our approach is to use a static, taint-style, dataflow analysis on the app bytecode in a novel way, to construct a high-level control flow graph that captures legal transitions among activities (app screens). We then use this graph to develop an exploration strategy named Targeted Exploration that permits fast, direct exploration of activities, including activities that would be difficult to reach during normal use. We also developed a strategy named Depth-first Exploration that mimics user actions for exploring activities and their constituents in a slower, but more systematic way. To measure the effectiveness of our techniques, we use two metrics: activity coverage (number of screens explored) and method coverage. Experiments with using our approach on 25 popular Android apps including BBC News, Gas Buddy, Amazon Mobile, YouTube, Shazam Encore, and CNN, show that our exploration techniques achieve 59.39-64.11% activity coverage and 29.53-36.46% method coverage.

Recording and replaying the execution of smartphone apps is useful in a variety of contexts, from reproducing bugs to profiling and testing. Achieving effective record-andreplay is a balancing act between accuracy and overhead. On smartphones, the act is particularly complicated, because smartphone apps receive a high-bandwidth stream of input (e.g., network, GPS, camera, microphone, touchscreen) and concurrency events, but the stream has to be recorded and replayed with minimal overhead, to avoid interfering with app execution. Prior record-and-replay approaches have focused on replaying machine instructions or system calls, which is not a good fit on smartphones. We propose a novel, stream-oriented record-and-replay approach which achieves high-accuracy and low-overhead by aiming at a sweet spot: recording and replaying sensor and network input, event schedules, and inter-app communication via intents. To demonstrate the versatility of our approach, we have constructed a tool named VALERA that supports record-and-replay on the Android platform. VALERA works with apps running directly on the phone, and does not require access to the app source code. Through an evaluation on 50 popular Android apps, we show that: VALERA's replay fidelity far exceeds current record-and-replay approaches for Android; VALERA's precise timing control and low overhead (about 1% for either record or replay) allows it to replay high-throughput, timing-sensitive apps such as video/audio capture and recognition; and VALERA's support for event schedule replay enables the construction of useful analyses, such as reproducing event-driven race bugs.

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