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About
Coverage.py is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not. Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not. Use coverage run to run your test suite and gather data. However you normally run your test suite, and you can run your test runner under coverage. If your test runner command starts with “python”, just replace the initial “python” with “coverage run”. To limit coverage measurement to code in the current directory, and also find files that weren’t executed at all, add the source argument to your coverage command line. By default, it will measure line (statement) coverage. It can also measure branch coverage. It can tell you what tests ran which lines.
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About
This module provides code coverage metrics for Perl. Code coverage metrics describe how thoroughly tests exercise code. By using Devel::Cover you can discover areas of code not exercised by your tests and determine which tests to create to increase coverage. Code coverage can be considered an indirect measure of quality. Devel::Cover is now quite stable and provides many of the features to be expected in a useful coverage tool. Statement, branch, condition, subroutine, and pod coverage information is reported. Statement and subroutine coverage data should be accurate. Branch and condition coverage data should be mostly accurate too, although not always what one might initially expect. Pod coverage comes from Pod::Coverage. If Pod::Coverage::CountParents is available it will be used instead.
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About
Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Any user looking for a solution to measure line and branch coverage to produce test reports
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Audience
Anyone looking for a powerful Code Coverage metric solution for Perl
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Audience
Engineers and data scientists requiring a solution to manage and improve their machine learning research
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationCoverage.py
United States
coverage.readthedocs.io/en/7.0.0/
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Company Informationmetacpan
metacpan.org/pod/Devel::Cover
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Company Informationscikit-learn
United States
scikit-learn.org/stable/
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Integrations
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Codecov
DagsHub
Databricks Data Intelligence Platform
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Integrations
C
Codecov
DagsHub
Databricks Data Intelligence Platform
Flower
GitHub
Go
Guild AI
Intel Tiber AI Studio
Keepsake
|
Integrations
C
Codecov
DagsHub
Databricks Data Intelligence Platform
Flower
GitHub
Go
Guild AI
Intel Tiber AI Studio
Keepsake
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