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.
Bugs in multi-threaded programs often arise due to data races. Numerous static and dynamic program analysis techniques have been proposed to detect data races. We propose a novel randomized dynamic analysis technique that utilizes potential data race information obtained from an existing analysis tool to separate real races from false races without any need for manual inspection. Specifically, we use potential data race information obtained from an existing dynamic analysis technique to control a random scheduler of threads so that real race conditions get created with very high probability and those races get resolved randomly at runtime. Our approach has several advantages over existing dynamic analysis tools. First, we can create a real race condition and resolve the race randomly to see if an error can occur due to the race. Second, we can replay a race revealing execution efficiently by simply using the same seed for random number generation--we do not need to record the execution. Third, our approach has very low overhead compared to other precise dynamic race detection techniques because we only track all synchronization operations and a single pair of memory access statements that are reported to be in a potential race by an existing analysis. We have implemented the technique in a prototype tool for Java and have experimented on a number of large multi-threaded Java programs. We report a number of previously known and unknown bugs and real races in these Java programs.
We present a novel dynamic analysis technique that finds real deadlocks in multi-threaded programs. Our technique runs in two stages. In the first stage, we use an imprecise dynamic analysis technique to find potential deadlocks in a multi-threaded program by observing an execution of the program. In the second stage, we control a random thread scheduler to create the potential deadlocks with high probability. Unlike other dynamic analysis techniques, our approach has the advantage that it does not give any false warnings. We have implemented the technique in a prototype tool for Java, and have experimented on a number of large multi-threaded Java programs. We report a number of previously known and unknown real deadlocks that were found in these benchmarks.
Foundational and scalable techniques for runtime safety analysis of multithreaded programs are explored in this paper. A technique based on vector clocks to extract the causal dependency order on state updates from a running multithreaded program is presented, together with algorithms to analyze a multithreaded computation against safety properties expressed using temporal logics. A prototype tool implementing our techniques, is also presented, together with examples where it can predict safety errors in multithreaded programs from successful executions of those programs. This tool is called Java MultiPathExplorer (JM-PaX), and available for download on the web. To the best of our knowledge, JMPaX is the first tool of its kind.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.