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LambdaTestLambdaTest is a cloud-based platform that facilitates cross-browser testing for enterprises, allowing them to execute web automation tests at scale through parallel coding functionality. **Selenium Automation Grid & Cypress CLI on LambdaTest** With LambdaTest, you have the capability to run tests across over 2,000 different browsers, devices, and operating systems, significantly enhancing browser coverage. This cloud-based Selenium Grid accelerates the execution of Selenium tests while ensuring a secure, scalable, and dependable environment. Additionally, the Cypress CLI offered by LambdaTest broadens your Cypress test coverage to more than 40 browser versions on both Windows and macOS. Beyond automation testing, LambdaTest also supports manual testing, visual interface testing, and real-time testing options. **LT Browser – Responsive Web Testing** The LT Browser by LambdaTest is an innovative tool designed for developers, enabling them to evaluate the responsiveness and usability of their websites effectively. Conducting mobile testing becomes more straightforward with the ability to perform responsive tests across over 50 resolutions, and users can create an unlimited number of custom devices tailored to their needs. This makes LambdaTest an invaluable resource for thorough web application testing across multiple environments.
What is Google ClusterFuzz?
ClusterFuzz is a comprehensive fuzzing framework aimed at identifying security weaknesses and stability issues within software applications. Used extensively by Google, it serves as the testing backbone for all its products and functions as the fuzzing engine for OSS-Fuzz. This powerful infrastructure comes equipped with numerous features that enable the seamless integration of fuzzing into the software development process. It offers fully automated procedures for filing bugs, triaging them, and resolving issues across various issue tracking platforms. Supporting multiple coverage-guided fuzzing engines, it enhances outcomes through ensemble fuzzing and a range of fuzzing techniques. Moreover, the system provides statistical data to evaluate the effectiveness of fuzzers and track the frequency of crashes. Users benefit from a user-friendly web interface that streamlines the management of fuzzing tasks and crash analysis. ClusterFuzz also accommodates various authentication methods via Firebase, and it boasts functionalities for black-box fuzzing, reducing test cases, and pinpointing regressions through bisection. In conclusion, this powerful tool not only elevates software quality and security but also becomes an essential asset for developers aiming to refine their applications, ultimately leading to more robust and reliable software solutions.
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
LibFuzzer
Firebase
Google OSS-Fuzz
Honggfuzz
Jira
Python
american fuzzy lop
Integrations Supported
LibFuzzer
Firebase
Google OSS-Fuzz
Honggfuzz
Jira
Python
american fuzzy lop
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
Company Website
github.com/google/clusterfuzz
Company Facts
Organization Name
Company Website
github.com/google/atheris