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DocmosisDocmosis is a versatile document generation solution that can be utilized either as a self-hosted option or through a SaaS model, allowing users to create templates tailored to their needs. It offers seamless integration with both custom-built software and well-known third-party applications via a comprehensive API. Users can design their templates using MS Word or LibreOffice, incorporating plain-text placeholders to manage the insertion of various elements such as text, images, and tables. Additionally, Docmosis allows for conditional content management, calculations, repetition of data, data formatting, and much more, enhancing the overall document creation process. This solution is compatible with diverse programming languages, including Java, C#, Python, PHP, and Ruby, through its REST API, and it easily connects with low-code and no-code platforms such as Appian, Bubble, Mendix, and Outsystems. Moreover, it works effectively with third-party form builders and applications that support webhooks, including FormAssembly and Salesforce. Businesses across many sectors—such as Finance, Health, Legal, Education, Government, HR, Insurance, Logistics, and Manufacturing—leverage Docmosis to produce a wide array of personalized documents, including letters, invoices, proposals, contracts, statements, and reports. By streamlining the document generation process, Docmosis empowers organizations to enhance efficiency and improve communication with their clients and stakeholders.
What is Echidna?
Echidna is a tool developed using Haskell that focuses on fuzzing and property-based testing for Ethereum smart contracts. It implements sophisticated grammar-driven fuzzing techniques that take advantage of a contract's ABI to test user-defined predicates or Solidity assertions. With its emphasis on modularity, Echidna is designed to be easily expandable, allowing developers to add new mutations or tailor the testing to specific contracts under various scenarios. The tool creates inputs that are finely tuned to your codebase, offering optional functionalities for corpus collection, mutation strategies, and coverage guidance to help identify subtle bugs. By utilizing Slither for the extraction of essential information before the fuzzing process begins, Echidna enhances the effectiveness of its testing. Its integration with source code allows for precise identification of which lines are executed during tests, accompanied by an interactive terminal UI and options for text-only or JSON output formats. Moreover, it features automatic minimization of test cases for more efficient bug triage and fits seamlessly into the overall development workflow. Echidna also tracks maximum gas consumption during fuzzing and accommodates complex contract initialization through Etheno and Truffle, thereby improving its practicality for developers. In conclusion, Echidna is a powerful tool that plays a vital role in ensuring the robustness and security of Ethereum smart contracts, making it an essential asset for developers in the blockchain space.
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
Docker
Etheno
Ethereum
GitHub
Google OSS-Fuzz
Haskell
Homebrew
JSON
LibFuzzer
Nix
Integrations Supported
Docker
Etheno
Ethereum
GitHub
Google OSS-Fuzz
Haskell
Homebrew
JSON
LibFuzzer
Nix
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
Crytic
Company Website
github.com/crytic/echidna
Company Facts
Organization Name
Company Website
github.com/google/atheris