Best Code Coverage Tools for Java

Parasoft helps organizations continuously deliver high-quality software with its AI-powered software testing platform and automated test solutions. Supporting embedded and enterprise markets, Parasoft’s proven technologies reduce the time, effort, and cost of delivering secure, reliable, and compliant software by integrating everything from deep code analysis and unit testing to UI and API testing, plus service virtualization and complete code coverage, into the delivery pipeline. A powerful unified C and C++ test automation solution for static analysis, unit testing and structural code coverage, Parasoft C/C++test helps satisfy compliance with industry functional safety and security requirements for embedded software systems.

IntelliJ IDEA is a professional-grade integrated development environment (IDE) primarily designed for Java and Kotlin development. It helps developers write code faster by automating routine tasks and providing smart coding assistance. The IDE supports the full software development lifecycle, from design and coding to testing and deployment. IntelliJ IDEA stays up to date with the latest language features, such as full support for Java 24 and Kotlin K2 mode. It offers a smooth and enjoyable workflow that helps developers stay focused and productive. The platform also emphasizes data privacy and security, complying with industry standards like SOC 2.

Xcode is Apple’s integrated development environment for building, testing, and distributing apps across Apple platforms. It provides powerful tools for coding, debugging, profiling, and simulation in one unified workspace. Predictive code completion and coding intelligence help developers write cleaner, faster code. Xcode supports advanced debugging and performance analysis to identify issues early. Built-in simulators allow developers to prototype apps across Apple devices without physical hardware. Testing frameworks ensure apps meet quality and performance standards. Xcode streamlines the entire app development lifecycle from idea to deployment.

Codacy is a comprehensive platform for code quality and security that helps development teams build secure, maintainable, and compliant software. It integrates across the entire development lifecycle, from IDE to production, providing real-time feedback and automated checks. Codacy analyzes code repositories, enforces quality standards, and detects vulnerabilities before deployment. With AI Guardrails, it also protects against risks introduced by AI-generated code. The platform centralizes rules and policies, ensuring consistency across teams and projects. Developers benefit from automated pull request checks, test coverage tracking, and actionable insights. Overall, Codacy enables faster development without compromising security or code quality.

Develop healthier code. Improve your code review workflow and quality. Codecov provides highly integrated tools to group, merge, archive, and compare coverage reports. Free for open source. Plans starting at $10/user per month. Ruby, Python, C++, Javascript, and more. Plug and play into any CI product and workflow. No setup required. Automatic report merging for all CI and languages into a single report. Get custom statuses on any group of coverage metrics. Review coverage reports by project, folder and type test (unit tests vs integration tests). Detailed report commented directly into your pull request. Codecov is SOC 2 Type II certified, which means a third-party audits and attests to our practices to secure our systems and your data.

DeepSource is an AI-powered code review platform designed to help development teams maintain high-quality, secure, and reliable code. The platform automates code reviews using a hybrid approach that combines static analysis with advanced AI agents. It integrates directly with development workflows through platforms like GitHub, GitLab, Bitbucket, and Azure DevOps. DeepSource analyzes pull requests in real time, identifying bugs, security vulnerabilities, code complexity issues, and maintainability risks before code reaches production. The system provides structured feedback and inline comments to help developers quickly understand and resolve issues. Additional features such as secrets detection, dependency vulnerability scanning, and infrastructure-as-code review strengthen application security. By automating repetitive review tasks and providing intelligent insights, DeepSource enables teams to ship software faster while maintaining strong code quality standards.

Debugging should be simple. AQTime Pro synthesizes complex memory and performance information into digestible, actionable insights so you can quickly find bugs and their root cause. Finding and squashing highly differentiated bugs is tedious and complicated, but AQTime Pro makes it easy. With over a dozen profilers, you can find memory leaks, performance bottlenecks, code coverage gaps and more in just a few clicks. AQTime Pro enables you to squash all bugs with one tool and get back to making high quality code. Don’t let code profilers box you in with a single codebase or framework and prevent you from finding performance bottlenecks, memory leaks, code coverage gaps unique to your project. AQTime Pro is the one tool to use across multiple codebases and frameworks in a project. It has broad language support for C/C++, Delphi, .NET, Java and more.

With a strong ecosystem of tools and APIs on major cloud providers, it is easier than ever to build services with Go. With popular open source packages and a robust standard library, use Go to create fast and elegant CLIs. With enhanced memory performance and support for several IDEs, Go powers fast and scalable web applications. With fast build times, lean syntax, an automatic formatter and doc generator, Go is built to support both DevOps and SRE. Everything there is to know about Go. Get started on a new project or brush up for your existing Go code. An interactive introduction to Go in three sections. Each section concludes with a few exercises so you can practice what you've learned. The Playground allows anyone with a web browser to write Go code that we immediately compile, link, and run on our servers.

JaCoCo is a free code coverage library for Java, which has been created by the EclEmma team based on the lessons learned from using and integrating existing libraries for many years. The master branch of JaCoCo is automatically built and published. Due to the test-driven development approach, every build is considered fully functional. See the change history for the latest features and bug fixes. SonarQube code quality metrics of the current JaCoCo implementation are available on SonarCloud.io. Integrate JaCoCo technology with your tools. Use JaCoCo tools out of the box. Improve the implementation and add new features. There are several open-source coverage technologies for Java available. While implementing the Eclipse plug-in EclEmma the observation was that none of them are really designed for integration. Most of them are specifically fit to a particular tool (Ant tasks, command line, IDE plug-in) and do not offer a documented API that allows embedding in different contexts.

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.

The JCov open-source project is used to gather quality metrics associated with the production of test suites. JCov is being opened in order to facilitate the practice of verifying test execution of regression tests in OpenJDK development. The main motivation behind JCov is the transparency of test coverage metrics. The advantage to promoting standard coverage based on JCov is that OpenJDK developers will be able to use a code coverage tool that stays in the 'lock step' with Java language and VM developments. JCov is a pure java implementation of a code coverage tool that provides a means to measure and analyze dynamic code coverage of Java programs. JCov provides functionality to collect method, linear block, and branch coverage, as well as show uncovered execution paths. It is also able to show a program's source code annotated with coverage information. From a testing perspective, JCov is most useful to determine execution paths.

Maximize your throughput and only release clean code SonarQube Cloud (formerly SonarCloud) automatically analyzes branches and decorates pull requests. Catch tricky bugs to prevent undefined behavior from impacting end-users. Fix vulnerabilities that compromise your app, and learn AppSec along the way with Security Hotspots. With just a few clicks you're up and running right where your code lives. Immediate access to the latest features and enhancements. Project dashboards keep teams and stakeholders informed on code quality and releasability. Display project badges and show your communities you're all about awesome. Code Quality and Code Security is a concern for your entire stack, from front-end to back-end. That’s why we cover 24 languages including Python, Java, C++, and many others. Transparency makes sense and that's why the trend is growing. Come join the fun, it's entirely free for open-source projects!

For many years Atlassian Clover has provided Java and Groovy developers a reliable source for code coverage analysis. This dependability has allowed us to focus our development efforts on delivering new features and improvements to our core offerings, including Jira Software, Bitbucket, and others. All of this has lead to our decision to open source Clover, what we believe is the best way to give Clover the focus and attention it deserves. Developers are ready and eager to contribute to Clover as they have with our other open-source projects including the IDE connectors and dozens of libraries. Although Clover is already a powerful code coverage tool we’re excited to see what the community will do to make it thrive.

The LDRA tool suite is LDRA’s flagship platform that delivers open and extensible solutions for building quality into software from requirements through to deployment. The tool suite provides a continuum of capabilities including requirements traceability, test management, coding standards compliance, code quality review, code coverage analysis, data-flow and control-flow analysis, unit/integration/target testing, and certification and regulatory support. The core components of the tool suite are available in several configurations that align with common software development needs. A comprehensive set of add-on capabilities are available to tailor the solution for any project. LDRA Testbed together with TBvision provide the foundational static and dynamic analysis engine, and a visualization engine to easily understand and navigate standards compliance, quality metrics, and code coverage analyses.

Testwell CTC++ is a powerful instrumentation-based code coverage and dynamic analysis tool for C and C++ code. With certain add-on components CTC++ can be used also on C#, Java and Objective-C code. Further, again with certain add-on components, CTC++ can be used to analyse code basically at any embedded target machines, also in very small ones (limited memory, no operating system). CTC++ provides Line Coverage, Statement Coverage, Function Coverage, Decision Coverage, Multicondition Coverage, Modified Condition/Decision Coverage (MC/DC), Condition Coverage. As a dynamic analysis tool, CTC++ shows the execution counters (how many times executed) in the code, i.e. more than a plain executed/not executed information. You can also use CTC++ to measure function execution costs (normally time) and to enable function entry/exit tracing at test time. CTC++ is easy to use.

Cobertura is a free Java tool that calculates the percentage of code accessed by tests. It can be used to identify which parts of your Java program are lacking test coverage. It is based on jcoverage. Cobertura is free software. Most of it is licensed under the GNU GPL, and you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Please review the file LICENSE.txt included in this distribution for further details.

We help you deliver code confidently by showing which parts of your code aren’t covered by your test suite. Free for open-source repositories. Pro accounts for private repositories. Instant sign-up through GitHub, Bitbucket, and Gitlab. Maintaining a well-tested codebase is mission-critical. Figuring out where your tests are lacking can be painful. You're already running your tests on a continuous integration server, so shouldn't it be doing the heavy lifting? Coveralls works with your CI server and sifts through your coverage data to find issues you didn't even know you had before they become a problem. If you're just running your code coverage locally, you won't be able to see changes and trends that occur during your entire development cycle. Coveralls lets you inspect every detail of your coverage with unlimited history. Coveralls takes the pain out of tracking your code coverage. Know where you stand with your untested code. Develop with confidence that your code is covered.

Our platform uses various security techniques, including coverage-guided and feedback-based fuzz testing, to automatically generate millions of test cases that trigger hard-to-find bugs deep within your application. This white-box approach protects against edge cases and speeds up development. Advanced fuzzing engines generate inputs that maximize code coverage. Powerful bug detectors check for errors during code execution. Uncover true vulnerabilities only. Get the input and stack trace as proof, so you can reliably reproduce errors every time. AI white-box testing uses data from all previous test runs to continuously learn the inner-workings of your application, triggering security-critical bugs with increasingly high precision.

RKTracer is a code-coverage and test-analysis tool that enables teams to assess the quality and completeness of their testing across unit, integration, functional, and system-level testing, without altering a single line of application code or build workflow. It supports instrumentation across host machines, simulators, emulators, embedded devices, and servers, and covers a broad array of programming languages, including C, C++, CUDA, C#, Java, Kotlin, JavaScript/TypeScript, Golang, Python, and Swift. It provides detailed coverage metrics such as function, statement, branch/decision, condition, MC/DC, and multi-condition coverage, and even supports delta-coverage reports to show which newly added or modified portions of code are already covered. Integration is seamless; simply prefix your build or test command with “rktracer”, run your tests, then generate HTML or XML reports (for CI/CD systems or dashboards like SonarQube).

Meet Agile development cycles while maintaining high-quality code. Use Jtest’s comprehensive set of Java testing tools to ensure defect-free coding through every stage of software development in the Java environment. Streamline Compliance With Security Standards. Ensure your Java code complies with industry security standards. Have compliance verification documentation automatically generated. Release Quality Software, Faster. Integrate Java testing tools to find defects faster and earlier. Save time and money by mitigating complicated and expensive problems down the line. Increase Your Return From Unit Testing. Achieve code coverage targets by creating a maintainable and optimized suite of JUnit tests. Get faster feedback from CI and within your IDE using smart test execution. Parasoft Jtest integrates tightly into your development ecosystem and CI/CD pipeline for real-time, intelligent feedback on your testing and compliance progress.

Codase hosts huge amount of open source codes providing a much better coverage, as it covers codes usually hidden inside compressed files and source control repositories, where general search engines fail to find and index. In addition, Codase only indexes and searches high quality codes with every line of code literally validated and compiled by intelligent and powerful source code analysis engine. Codase is a privately held company based in silicon valley, founded by Dr. Huihong Luo and other veterans. We are a group of innovative and passionate professionals with diverse technologies, business background and proven track of records. We are committed to make Codase the world's best search engine for source codes in terms of features, quality, performance and code coverage. If you are a developer, you may find Codase useful, since improving your coding productivity is our goal.