Best Code Coverage Tools for JUnit

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 JetBrains IDE for professional development in Java and Kotlin. It unlocks productive development and helps you write high-quality code with ease. Built to get the job done, it provides all of the essential tools and cutting-edge technology support you need. With a smooth, comfortable workflow and a strong focus on privacy and security, it lets you code with confidence and pleasure.

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.

Coco® is a multi-language code coverage tool. Automatic source code instrumentation is used to measure test coverage of statements, branches and conditions. Executing a test suite against an instrumented application produces data that can later be analyzed. This analysis can be used to understand how much of the source code has been hit by tests, which additional tests need to be written, how the test coverage changed over time and more. Identify redundant tests, untested or dead code. Identify the impact of a patch on the code and code coverage & your testing. Coco supports statement coverage, branch coverage, MC/DC and other levels. Linux, Windows, RTOS and others. Using GCC, Visual Studio, embedded compilers and more. Choice of different report formats (text, HTML, XML, JUnit, Cobertura). Coco can also be integrated with various build, test and CI frameworks like JUnit, Jenkins and SonarQube.

Linux, Windows, RTOS and others. Using gcc, Visual Studio, embedded compilers and more. Merging multiple execution reports to provide advanced analysis and more outstanding features. Assess and optimize code performance with Coco’s built-in Function Profiler.

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.

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.

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.