Higson.io

Website

higson.io

Pricing URL

Official Higson.io Pricing

$ Details

paid Free Trial

Categories

#Business & Commerce #Personalization #Data Dashboard #Rules Engine

assertpy

Website

github.com

Pricing URL

-

$ Details

-

Categories

#Testing #Python

Higson.io features and specs

• Structure
  Intuitive tree structure for your rules. Our structure corresponds with your business, so it’s super easy to navigate.
• Performance
  Higson Engine represents a significant leap forward in performance, scalability, and resource optimization.  
• Publish changes without deployment
  The application using Higson is updated without the need for releasing a new version of the application.
• Tester
  HIgson provides testing module for parameters, functions and domain elements. It means that every change done by a user may be verified before publishing.
• Versionning
  A feature for saving states of decision tables, functions, and business rules at specific moments.
• Decision table
  The easy tounderstand matrix that matches input data with a decision. They look very trivial; however you can achieve complex configurations using them, which is their power - everybody can understand how to model decisions using them.
• Functions
  In some cases, you need to write more complex logic. In Higson, you can use Groovy language - very simple for simple logic, yet powerful. So powerful that You can ask your IT department for help implementing complex things that require loops and other complex techniques.
• Batch Tester
  An advanced testing tool for mass validation of multiple business rules, decision tables, or functions.
• Flows
  Visual Rule Modeling

assertpy features and specs

• Fluent API
  Assertpy offers a fluent API that makes assertions more readable and expressive, enabling developers to write assertions in a natural language style that is easy to understand.
• Chainable Assertions
  It allows for chainable assertions, enabling multiple checks to be performed in a single line of code, thereby reducing verbosity and enhancing clarity.
• Comprehensive Assertion Methods
  The library provides a wide range of built-in assertion methods, catering to various types of data validations, such as checking for size, type, value, and more.
• Extensibility
  Assertpy supports extending its functionality by defining custom assertions, allowing developers to tailor it to their specific needs.
• Pythonic
  Designed with Pythonic principles in mind, Assertpy fits seamlessly into Python projects, enabling idiomatic and consistent code style.

Possible disadvantages of assertpy

• Learning Curve
  Developers new to the library may encounter a learning curve due to the distinct approach of using fluent and chainable assertions as opposed to traditional methods.
• Limited by Python Version
  The library may have limitations in terms of compatibility with older versions of Python, requiring users to ensure their environment is up-to-date.
• Performance Overhead
  The additional abstraction layer introduced by a fluent interface might introduce some performance overhead, especially in performance-critical or resource-constrained environments.
• Less Community Support
  Compared to more established testing libraries, Assertpy might have less community support and fewer resources available for resolving issues or getting help.
• Dependency Management
  Using a third-party library introduces additional dependencies to manage, which could complicate project maintenance and compatibility.

Analysis of assertpy

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