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features
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Description

Coco is a comprehensive code coverage solution designed for modern software development across both embedded systems and desktop applications. It empowers developers, QA engineers, and compliance teams to measure and improve test coverage through function, branch, decision, condition, and MC/DC coverage metrics. With support for multiple languages and toolchains—including GCC, Clang, MSBuild, ARM, QNX, and Green Hills—Coco integrates seamlessly into existing CI/CD workflows without requiring code refactoring. Teams can quickly detect coverage gaps, streamline regression testing, and remove redundant test cases to shorten validation cycles. For regulated industries like automotive, aerospace, and healthcare, Coco delivers qualification kits and pre-built certification artifacts to support ISO 26262 and DO-178C compliance. The Coco Cross-Compilation Add-on extends capabilities to embedded Linux, RTOS, and bare-metal targets, offering full traceability from test execution to certification. Its integration with Test Center provides real-time analytics, visualization, and organization-wide reporting for test intelligence. With Coco, development teams gain transparency, speed, and trust in every release cycle.

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
Azure Marketplace
BlackBerry QNX
C#
C++
Coverity Static Analysis
Git
GitLab
Java
Jenkins
Jira
NUnit
QML
SystemC
Tcl
TeamCity
TestRail
VxWorks
XML

Integrations

AWS Marketplace
Amazon Corretto
Azure Marketplace
BlackBerry QNX
C#
C++
Coverity Static Analysis
Git
GitLab
Java
Jenkins
Jira
NUnit
QML
SystemC
Tcl
TeamCity
TestRail
VxWorks
XML

Pricing Details

No price information available.
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

Qt Group

Founded

1994

Country

Finland

Website

www.qt.io/quality-assurance/coco

Vendor Details

Company Name

OpenJDK

Country

United States

Website

wiki.openjdk.org/display/CodeTools/jcov

Product Features

Static Code Analysis

Analytics / Reporting
Code Standardization / Validation
Multiple Programming Language Support
Provides Recommendations
Standard Security/Industry Libraries
Vulnerability Management

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