Automated software testing aims to detect errors by producing test inputs that cover as much of the application source code as possible. Applications for mobile devices are typically event-driven, which raises the challenge of automatically producing event sequences that result in high coverage. Some existing approaches use random or model-based testing that largely treats the application as a black box. Other approaches use symbolic execution, either starting from the entry points of the applications or on specific event sequences. A common limitation of the existing approaches is that they often fail to reach the parts of the application code that require more complex event sequences.We propose a two-phase technique for automatically finding event sequences that reach a given target line in the application code. The first phase performs concolic execution to build summaries of the individual event handlers of the application. The second phase builds event sequences backward from the target, using the summaries together with a UI model of the application. Our experiments on a collection of open source Android applications show that this technique can successfully produce event sequences that reach challenging targets.
Modern event-driven applications, such as, web pages and mobile apps, rely on asynchrony to ensure smooth end-user experience. Unfortunately, even though these applications are executed by a single event-loop thread, they can still exhibit nondeterministic behaviors depending on the execution order of interfering asynchronous events. As in classic shared-memory concurrency, this nondeterminism makes it challenging to discover errors that manifest only in specific schedules of events.In this work we propose the first stateless model checker for event-driven applications, called R4. Our algorithm systematically explores the nondeterminism in the application and concisely exposes its overall effect, which is useful for bug discovery. The algorithm builds on a combination of three key insights: (i) a dynamic partial order reduction (DPOR) technique for reducing the search space, tailored to the domain of event-driven applications, (ii) conflict-reversal bounding based on a hypothesis that most errors occur with a small number of event reorderings, and (iii) approximate replay of event sequences, which is critical for separating harmless from harmful nondeterminism.We instantiate R4 for the domain of client-side web applications and use it to analyze event interference in a number of real-world programs. The experimental results indicate that the precision and overall exploration capabilities of our system significantly exceed that of existing techniques.
We present a novel tool for statically determining the Worst Case Execution Time (WCET) of Java Bytecode-based programs called Tool for Execution Time Analysis of Java bytecode (TetaJ). This tool differentiates itself from existing tools by separating the individual constituents of the execution environment into independent components. The prime benefit is that it can be used for execution environments featuring common embedded processors and software implementations of the JVM. TetaJ employs a model checking approach for statically determining WCET where the Java program, the JVM, and the hardware are modelled as Networks of Timed Automata (NTA) and given as input to the state-of-the-art UPPAAL model checking tool. The tool is evaluated through a case study based on the classic text-book example of a hard realtime control system in a mine pump. The system is hosted on an execution environment featuring an interpretationbased JVM, called Hardware near Virtual Machine (HVM) that runs on an Atmel AVR ATmega2560 processor.
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.