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QA WolfQA Wolf empowers engineering teams to achieve an impressive 80% automated test coverage for end-to-end processes within a mere four months. Here’s what you can expect to receive, regardless of whether you need 100 tests or 100,000: • Achieve automated end-to-end testing for 80% of user flows in just four months, with tests crafted using Playwright, an open-source tool ensuring you have full ownership of your code without vendor lock-in. • A comprehensive test matrix and outline structured within the AAA framework. • The capability to conduct unlimited parallel testing across any environment you prefer. • Infrastructure for 100% parallel-run tests, which is hosted and maintained by us. • Ongoing support for flaky and broken tests within a 24-hour window. • Assurance of 100% reliable results with absolutely no flaky tests. • Human-verified bug reports delivered through your preferred messaging app. • Seamless CI/CD integration with your deployment pipelines and issue trackers. • Round-the-clock access to dedicated QA Engineers at QA Wolf to assist with any inquiries or issues. With this robust support system in place, teams can confidently scale their testing efforts while improving overall software quality.
What is afl-unicorn?
AFL-Unicorn enables the fuzzing of any binary that can be emulated with the Unicorn Engine, providing the ability to focus on specific code segments during testing. As long as the desired code can be emulated using the Unicorn Engine, AFL-Unicorn can be utilized effectively for fuzzing tasks. The Unicorn Mode features block-edge instrumentation akin to AFL's QEMU mode, allowing AFL to collect block coverage data from the emulated code segments, which is essential for its input generation process. This functionality is contingent upon the meticulous configuration of a Unicorn-based test harness, which plays a crucial role in loading the intended code, setting up the initial state, and integrating data altered by AFL from its storage. Once these parameters are established, the test harness simulates the target binary code, and upon detecting a crash or error, it sends a signal to indicate the problem. Although this framework has been primarily validated on Ubuntu 16.04 LTS, it is built to work seamlessly with any operating system that can support both AFL and Unicorn. By utilizing this framework, developers can significantly enhance their fuzzing strategies and streamline their binary analysis processes, leading to more effective vulnerability detection and software reliability improvements. This broader compatibility opens up new opportunities for developers to adopt advanced fuzzing techniques across various platforms.
What is Atheris?
Atheris operates as a fuzzing engine tailored for Python, specifically employing a coverage-guided approach, and it extends its functionality to accommodate native extensions built for CPython. Leveraging libFuzzer as its underlying framework, Atheris proves particularly adept at uncovering additional bugs within native code during fuzzing processes. It is compatible with both 32-bit and 64-bit Linux platforms, as well as Mac OS X, and supports Python versions from 3.6 to 3.10. While Atheris integrates libFuzzer, which makes it well-suited for fuzzing Python applications, users focusing on native extensions might need to compile the tool from its source code to align the libFuzzer version included with Atheris with their installed Clang version. Given that Atheris relies on libFuzzer, which is bundled with Clang, users operating on Apple Clang must install an alternative version of LLVM, as the standard version does not come with libFuzzer. Atheris utilizes a coverage-guided, mutation-based fuzzing strategy, which streamlines the configuration process, eliminating the need for a grammar definition for input generation. However, this approach can lead to complications when generating inputs for code that manages complex data structures. Therefore, users must carefully consider the trade-offs between the simplicity of setup and the challenges associated with handling intricate input types, as these factors can significantly influence the effectiveness of their fuzzing efforts. Ultimately, the decision to use Atheris will hinge on the specific requirements and complexities of the project at hand.
Integrations Supported
Google OSS-Fuzz
LibFuzzer
Python
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Free Trial Offered?
Free Version
Pricing Information
Free
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Battelle
Company Website
github.com/Battelle/afl-unicorn
Company Facts
Organization Name
Company Website
github.com/google/atheris