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

NCover Desktop is a Windows application that helps you collect code coverage statistics for .NET applications and services. After coverage is collected, Desktop displays charts and coverage metrics in a browser-based GUI that allows you to drill all the way down to your individual lines of source code. Desktop also allows you the option to install a Visual Studio extension called Bolt. Bolt offers built-in code coverage that displays unit test results, timings, branch visualization and source code highlighting right in the Visual Studio IDE. NCover Desktop is a major leap forward in the ease and flexibility of code coverage tools. Code coverage, gathered while testing your .NET code, shows the NCover user what code was exercised during the test and gives a specific measurement of unit test coverage. By tracking these statistics over time, you gain a concrete measurement of code quality during the development cycle.

About

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

About

VisionPro Deep Learning is the best-in-class deep learning-based image analysis software designed for factory automation. Its field-tested algorithms are optimized specifically for machine vision, with a graphical user interface that simplifies neural network training without compromising performance. VisionPro Deep Learning solves complex applications that are too challenging for traditional machine vision alone, while providing a consistency and speed that aren’t possible with human inspection. When combined with VisionPro’s rule-based vision libraries, automation engineers can easily choose the best the tool for the task at hand. VisionPro Deep Learning combines a comprehensive machine vision tool library with advanced deep learning tools inside a common development and deployment framework. It simplifies the development of highly variable vision applications.

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

Development teams searching for a powerful .NET Code Coverage solution

Audience

Developers interested in a beautiful but advanced programming language

Audience

Any kind of company searching for a factory automation via Artificial Intelligence, rule based algorithms and deep-learning solution

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

No information available.
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

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

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

Reviews/Ratings

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

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

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

NCover
www.ncover.com

Company Information

Python
Founded: 1991
www.python.org

Company Information

Cognex
Founded: 1981
United States
www.cognex.com/products/deep-learning/visionpro-deep-learning

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NeuralVision

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Devel::Cover

Devel::Cover

metacpan
Devel::Cover

Devel::Cover

metacpan

Categories

Categories

Categories

Categories

Deep Learning Features

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Integrations

Agenta
Amazon Q
Cython
DeepSeek
Firepad
Gerrit Code Review
Grok 4
IntelliSense
Kolena
LoginID
NetsPresso
Open Interpreter
OpenSVC
Parallel Domain Replica Sim
Piloterr
PromptGround
PyBullet
SQLite
Vald
american fuzzy lop

Integrations

Agenta
Amazon Q
Cython
DeepSeek
Firepad
Gerrit Code Review
Grok 4
IntelliSense
Kolena
LoginID
NetsPresso
Open Interpreter
OpenSVC
Parallel Domain Replica Sim
Piloterr
PromptGround
PyBullet
SQLite
Vald
american fuzzy lop

Integrations

Agenta
Amazon Q
Cython
DeepSeek
Firepad
Gerrit Code Review
Grok 4
IntelliSense
Kolena
LoginID
NetsPresso
Open Interpreter
OpenSVC
Parallel Domain Replica Sim
Piloterr
PromptGround
PyBullet
SQLite
Vald
american fuzzy lop

Integrations

Agenta
Amazon Q
Cython
DeepSeek
Firepad
Gerrit Code Review
Grok 4
IntelliSense
Kolena
LoginID
NetsPresso
Open Interpreter
OpenSVC
Parallel Domain Replica Sim
Piloterr
PromptGround
PyBullet
SQLite
Vald
american fuzzy lop
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