JCov

JCov

OpenJDK
VBScript

VBScript

Microsoft

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.

About

The JCov open-source project is used to gather quality metrics associated with the production of test suites. JCov is being opened in order to facilitate the practice of verifying test execution of regression tests in OpenJDK development. The main motivation behind JCov is the transparency of test coverage metrics. The advantage to promoting standard coverage based on JCov is that OpenJDK developers will be able to use a code coverage tool that stays in the 'lock step' with Java language and VM developments. JCov is a pure java implementation of a code coverage tool that provides a means to measure and analyze dynamic code coverage of Java programs. JCov provides functionality to collect method, linear block, and branch coverage, as well as show uncovered execution paths. It is also able to show a program's source code annotated with coverage information. From a testing perspective, JCov is most useful to determine execution paths.

About

VBScript is a programming language included with Microsoft Internet Explorer. For other browsers, contact your vendor about support. VBScript 2.0 (or later) is recommended for use with Agent. Although earlier versions of VBScript may work with Agent, they lack certain functions that you may want to use. You can download VBScript 2.0 and obtain further information on VBScript at the Microsoft Downloads site and the Microsoft VBScript site. With VBScript (2.0 or later), you can verify whether Microsoft Agent is installed by trying to create the object and checking to see if it exists. The following sample demonstrates how to check for the Agent control without triggering an auto-download of the control.

About

scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image provides a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! scikit-image aims to be the reference library for scientific image analysis in Python. We accomplish this by being easy to use and install. We are careful in taking on new dependencies, and sometimes cull existing ones, or make them optional. All functions in our API have thorough docstrings clarifying expected inputs and outputs. Conceptually identical arguments have the same name and position in a function signature. Test coverage is close to 100% and code is reviewed by at least two core developers before being included in the library.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Any user looking for a solution to measure line and branch coverage to produce test reports

Audience

Developers seeking a solution to gather quality metrics associated with the production of test suites

Audience

Programming Language solution for developers

Audience

Developers and professionals requiring a free solution offering algorithms for their image processing projects

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 3.0 / 5
features 5.0 / 5
design 5.0 / 5
support 3.0 / 5

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Coverage.py
United States
coverage.readthedocs.io/en/7.0.0/

Company Information

OpenJDK
United States
wiki.openjdk.org/display/CodeTools/jcov

Company Information

Microsoft
docs.microsoft.com/en-us/windows/win32/lwef/using-vbscript

Company Information

scikit-image
United States
scikit-image.org

Alternatives

Alternatives

Alternatives

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Categories

Categories

Categories

Categories

Integrations

Advanced Email Parser
CodePal
Cython
Django
EditPlus
HTML
Label Studio
Mako
PostgresML
Python
Red Hat Runtimes
SlickEdit
System Frontier
UEStudio
XML
Yamak.ai
ZenML
phase 5

Integrations

Advanced Email Parser
CodePal
Cython
Django
EditPlus
HTML
Label Studio
Mako
PostgresML
Python
Red Hat Runtimes
SlickEdit
System Frontier
UEStudio
XML
Yamak.ai
ZenML
phase 5

Integrations

Advanced Email Parser
CodePal
Cython
Django
EditPlus
HTML
Label Studio
Mako
PostgresML
Python
Red Hat Runtimes
SlickEdit
System Frontier
UEStudio
XML
Yamak.ai
ZenML
phase 5

Integrations

Advanced Email Parser
CodePal
Cython
Django
EditPlus
HTML
Label Studio
Mako
PostgresML
Python
Red Hat Runtimes
SlickEdit
System Frontier
UEStudio
XML
Yamak.ai
ZenML
phase 5
Claim Coverage.py and update features and information
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