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features
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support

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Description

Deep Cover strives to be the premier tool for Ruby code coverage, delivering enhanced accuracy for both line and branch coverage metrics. It serves as a seamless alternative to the standard Coverage library, providing a clearer picture of code execution. A line is deemed covered only when it has been fully executed, and the optional branch coverage feature identifies any branches that remain untraveled. The MRI implementation considers all methods available, including those created through constructs like define_method and class_eval. Unlike Istanbul's method, DeepCover encompasses all defined methods and blocks when reporting coverage. Although loops are not classified as branches within DeepCover, accommodating them can be easily arranged if necessary. Even once DeepCover is activated and set up, it requires only a minimal amount of code loading, with coverage tracking starting later in the process. To facilitate an easy migration for projects that have previously relied on the built-in Coverage library, DeepCover can integrate itself into existing setups, ensuring a smooth transition for developers seeking improved coverage analysis. This capability makes DeepCover not only versatile but also user-friendly for teams looking to enhance their testing frameworks.

Description

The JCov open-source initiative is designed to collect quality metrics related to the development of test suites. By making JCov accessible, the project aims to enhance the verification of regression test executions within OpenJDK development. The primary goal of JCov is to ensure transparency regarding test coverage metrics. Promoting a standard coverage tool like JCov benefits OpenJDK developers by providing a code coverage solution that evolves in harmony with advancements in the Java language and VM. JCov is entirely implemented in Java and serves as a tool to assess and analyze dynamic code coverage for Java applications. It offers features that measure method, linear block, and branch coverage, while also identifying execution paths that remain uncovered. Additionally, JCov can annotate the program's source code with coverage data. From a testing standpoint, JCov is particularly valuable for identifying execution paths and understanding how different pieces of code are exercised during testing. This detailed insight helps developers enhance their testing strategies and improve overall code quality.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace
Amazon Corretto
Apache NetBeans
Azure Marketplace
Code Climate
Codecov
Coveralls
Helidon
Istanbul
Java
Red Hat Runtimes
Ruby
SimpleCov
XML

Integrations

AWS Marketplace
Amazon Corretto
Apache NetBeans
Azure Marketplace
Code Climate
Codecov
Coveralls
Helidon
Istanbul
Java
Red Hat Runtimes
Ruby
SimpleCov
XML

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

DeepCover

Country

United States

Website

github.com/deep-cover/deep-cover

Vendor Details

Company Name

OpenJDK

Country

United States

Website

wiki.openjdk.org/display/CodeTools/jcov

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