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Description

CI Fuzz guarantees that your code is both robust and secure, achieving test coverage levels as high as 100%. You can utilize CI Fuzz through the command line or within your preferred integrated development environment (IDE) to automatically generate a vast number of test cases. Similar to a unit test, CI Fuzz analyzes code during execution, leveraging AI to ensure every code path is effectively covered. This tool helps you identify genuine bugs in real-time, eliminating the need to deal with hypothetical problems and erroneous positives. It provides all the necessary details to help you swiftly reproduce and resolve actual issues. By maximizing your code coverage, CI Fuzz also automatically identifies common security vulnerabilities, such as injection flaws and remote code execution risks, all in a single process. Ensure your software is of the highest quality by achieving comprehensive test coverage. With CI Fuzz, you can elevate your unit testing practices, as it harnesses AI for thorough code path analysis and the seamless creation of numerous test cases. Ultimately, it enhances your pipeline's efficiency without sacrificing the integrity of the software being produced. This makes CI Fuzz an essential tool for any developer aiming to improve code quality and security.

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

Java
AWS Marketplace
Amazon Corretto
Apache NetBeans
Azure Marketplace
C
C++
CLion
Helidon
JUnit
JavaScript
Jest
Kotlin
OWASP WSFuzzer
Red Hat Runtimes
TypeScript
Visual Studio
XML

Integrations

Java
AWS Marketplace
Amazon Corretto
Apache NetBeans
Azure Marketplace
C
C++
CLion
Helidon
JUnit
JavaScript
Jest
Kotlin
OWASP WSFuzzer
Red Hat Runtimes
TypeScript
Visual Studio
XML

Pricing Details

€30 per month
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

Code Intelligence

Country

Germany

Website

www.code-intelligence.com/product-ci-fuzz

Vendor Details

Company Name

OpenJDK

Country

United States

Website

wiki.openjdk.org/display/CodeTools/jcov

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