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What is pytest?

Pytest serves as an essential resource for improving your coding abilities, enabling the easy development of both simple tests and more intricate functional tests across a variety of applications and libraries. The framework excels in offering comprehensive assertion introspection, allowing you to depend solely on standard assert statements for all your testing requirements. It provides extensive insights into failed assertions, automatically detects test modules and functions, and includes versatile fixtures that efficiently manage both small and parameterized long-lived test resources. Moreover, pytest can effortlessly run unittest (including trial) and nose test suites, and it supports Python versions 3.6 and later, as well as PyPy 3. Its extensive plugin ecosystem includes over 315 external plugins, alongside a dynamic community of users contributing to its growth. Additionally, the maintainers of pytest, in collaboration with Tidelift, offer commercial support and maintenance for the open-source dependencies crucial to your projects, further enhancing its value. By integrating pytest into your workflow, you can streamline your testing process, reduce potential risks, and improve the overall quality of your codebase, all while ensuring fair compensation for the developers of the dependencies you utilize. This dedication to community engagement and support distinctly positions pytest as a frontrunner in the realm of testing frameworks, making it a preferred choice among developers.

What is AutoDiscovery?

AutoDiscovery is a sophisticated software solution crafted for automated exploratory data analysis, enabling biomedical researchers to uncover complex relationships hidden within their experimental and clinical trial data. This innovative tool autonomously identifies the appropriate statistical tests required to assess the connections between different variable combinations in each data subset. AutoDiscovery effectively tackles prevalent challenges in biomedical research, such as discerning cause-effect relationships, managing false discovery rates, addressing small sample sizes, organizing treatment groups, and ensuring result traceability. Specifically designed for Principal Investigators, who often face time constraints and may lack extensive statistical knowledge, it allows them to focus on conducting significant and productive research endeavors. Additionally, this software streamlines research workflows, promoting faster insights and breakthroughs in the biomedical domain while empowering researchers to make data-driven decisions with confidence. By leveraging AutoDiscovery, researchers can enhance the overall quality of their findings and contribute more effectively to advancements in health science.

Media

Media

Integrations Supported

Allure Report
Captain
Codecov
Coverage.py
Katalon Recorder
Katalon True Platform
Launchable
Opik
Pynt
Python
Roost.ai
TestQuality
Testmo
VIKTOR
Zebrunner
pytest-cov

Integrations Supported

Allure Report
Captain
Codecov
Coverage.py
Katalon Recorder
Katalon True Platform
Launchable
Opik
Pynt
Python
Roost.ai
TestQuality
Testmo
VIKTOR
Zebrunner
pytest-cov

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

€1.795 per year
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

pytest

Date Founded

2004

Company Website

docs.pytest.org/en/6.2.x/

Company Facts

Organization Name

Butler Scientifics

Date Founded

2014

Company Location

Spain

Company Website

www.butlerscientifics.com

Categories and Features

Functional Testing

Automated Testing
Interface Testing
Regression Testing
Reporting / Analytics
Sanity Testing
Smoke Testing
System Testing
Unit Testing

Software Testing

Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing

Categories and Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

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