Boozang
Simplified Testing Without Code
Empower every member of your team, not just developers, to create and manage automated tests effortlessly.
Address your testing needs efficiently, achieving comprehensive test coverage in mere days instead of several months.
Our tests designed in natural language are highly resilient to changes in the codebase, and our AI swiftly fixes any test failures that may arise.
Continuous Testing is essential for Agile and DevOps practices, allowing you to deploy features to production within the same day.
Boozang provides various testing methods, including:
- A Codeless Record/Replay interface
- BDD with Cucumber
- API testing capabilities
- Model-based testing
- Testing for HTML Canvas
The following features streamline your testing process:
- Debugging directly within your browser console
- Screenshots pinpointing where tests fail
- Seamless integration with any CI server
- Unlimited parallel testing to enhance speed
- Comprehensive root-cause analysis reports
- Trend reports to monitor failures and performance over time
- Integration with test management tools like Xray and Jira, making collaboration easier for your team.
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RetailEdge
RetailEdge is an intuitive and comprehensive point of sale (POS) and inventory management software tailored for retail enterprises, developed by High Meadow Business Solutions. This platform encompasses multi-location capabilities, seamless credit card processing, website integration, and mobile POS functionality, alongside gift card management features. It also supports secure mobile payment options like Apple Pay and EMV, while integrating with various e-commerce platforms for streamlined order processing, price adjustments, and gift card management tasks.
What sets us apart?
1. A one-time payment for the software eliminates ongoing fees.
2. The hybrid software architecture keeps all data locally stored, ensuring quick real-time access even during internet outages or slow connections.
3. It includes a complimentary hour of training with real experts, aimed at organizing your inventory effectively and guiding you through the myriad of robust tools available to enhance your business growth.
4. Optional ongoing support and updates are tailored to meet your business requirements affordably.
5. Our integrated credit card processing is equipped with the latest features, designed to secure the lowest transaction fees, enabling you to maximize your savings.
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Google OSS-Fuzz
OSS-Fuzz offers continuous fuzz testing for open-source software, a technique well-regarded for uncovering coding errors. These errors, such as buffer overflow vulnerabilities, can lead to serious security threats. By utilizing guided in-process fuzzing on Chrome components, Google has identified thousands of security flaws and stability concerns, with plans to broaden the reach of this valuable service to the open-source community. The main goal of OSS-Fuzz is to improve the security and stability of widely utilized open-source software by merging sophisticated fuzzing techniques with an adaptable and distributed framework. For those projects that do not qualify for OSS-Fuzz, alternatives like personal instances of ClusterFuzz or ClusterFuzzLite are available. Currently, OSS-Fuzz supports programming languages such as C/C++, Rust, Go, Python, and Java/JVM, and it may extend its support to additional languages that work with LLVM. Additionally, OSS-Fuzz enables fuzzing for both x86_64 and i386 architecture builds, allowing a diverse array of applications to take advantage of this cutting-edge testing methodology. This initiative aims not only to enhance software quality but also to contribute to the creation of a more secure software ecosystem for every user involved. Such improvements can lead to greater trust in open-source solutions.
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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.
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