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About

Balance your effort spent on writing applications and test code. Use the most sophisticated code coverage tool for Java and Groovy. OpenClover measures code coverage for Java and Groovy and collects over 20 code metrics. It not only shows you untested areas of your application but also combines coverage and metrics to find the riskiest code. The Test Optimization feature tracks which test cases are related to each class of your application code. Thanks to this OpenClover can run tests relevant to changes made in your application code, significantly reducing test execution time. Do testing getters and setters bring much value? Or machine-generated code? OpenClover outruns other tools in its flexibility to define the scope of coverage measurement. You can exclude packages, files, classes, methods, and even single statements. You can focus on testing important parts of your code. OpenClover not only records test results but also measures individual code coverage for every test.

About

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. ggplot2 is now over 10 years old and is used by hundreds of thousands of people to make millions of plots. That means, by-and-large, ggplot2 itself changes relatively little. When we do make changes, they will be generally to add new functions or arguments rather than changing the behavior of existing functions, and if we do make changes to existing behavior we will do them for compelling reasons. If you are new to ggplot2 you are better off starting with a systematic introduction, rather than trying to learn from reading individual documentation pages.

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.

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

Professionals seeking a tool to measures code and collect over 20 code metrics

Audience

Developers searching for a powerful Component Libraries solution

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

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

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

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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

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

OpenClover
United States
openclover.org

Company Information

ggplot2
ggplot2.tidyverse.org

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

Alternatives

Alternatives

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Gensim

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JCov

JCov

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ML.NET

ML.NET

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MLlib

MLlib

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Orange

Orange

University of Ljubljana
Devel::Cover

Devel::Cover

metacpan
Keepsake

Keepsake

Replicate

Categories

Categories

Categories

Integrations

Apache Ant
Apache Groovy
Codecov
DagsHub
Eclipse IDE
Flower
Grails
Guild AI
HTML
Intel Tiber AI Studio
JSON
JUnit
Jira
Keepsake
MLJAR Studio
Matplotlib
Python
R
TestNG
XML

Integrations

Apache Ant
Apache Groovy
Codecov
DagsHub
Eclipse IDE
Flower
Grails
Guild AI
HTML
Intel Tiber AI Studio
JSON
JUnit
Jira
Keepsake
MLJAR Studio
Matplotlib
Python
R
TestNG
XML

Integrations

Apache Ant
Apache Groovy
Codecov
DagsHub
Eclipse IDE
Flower
Grails
Guild AI
HTML
Intel Tiber AI Studio
JSON
JUnit
Jira
Keepsake
MLJAR Studio
Matplotlib
Python
R
TestNG
XML
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