Best Code Coverage Tools in Germany

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.

All the Python tools in one place. Save time while PyCharm takes care of the routine. Focus on the bigger things and embrace the keyboard-centric approach to get the most of PyCharm's many productivity features. PyCharm knows everything about your code. Rely on it for intelligent code completion, on-the-fly error checking and quick-fixes, easy project navigation, and much more. Write neat and maintainable code while the IDE helps you keep control of the quality with PEP8 checks, testing assistance, smart refactorings, and a host of inspections. PyCharm is designed by programmers, for programmers, to provide all the tools you need for productive Python development. PyCharm provides smart code completion, code inspections, on-the-fly error highlighting and quick-fixes, along with automated code refactorings and rich navigation capabilities.

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!

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 helps you automatically find and fix issues in your code during code reviews, such as bug risks, anti-patterns, performance issues, and security flaws. It takes less than 5 minutes to set up with your Bitbucket, GitHub, or GitLab account. It works for Python, Go, Ruby, and JavaScript. DeepSource covers all major programming languages, Infrastructure-as-Code, secrets detection, code coverage, and more. You won't need any other tool to protect your code. Start building with the most sophisticated static analysis platform for your workflow and prevent bugs before they end up in production. Largest collection of static analysis rules in the industry. Your team's central hub to track and take action on code health. Put code formatting on autopilot. Never let your CI break on style violations. Automatically generates and applies fixes for issues in a couple of clicks.

Tarpaulin is a code coverage reporting tool for the cargo build system, named for a waterproof cloth used to cover cargo on a ship. Currently, tarpaulin provides working line coverage and while fairly reliable may still contain minor inaccuracies in the results. A lot of work has been done to get it working on a wide range of projects, but often unique combinations of packages and build features can cause issues so please report anything you find that's wrong. Also, check out our roadmap for planned features. On Linux Tarpaulin's default tracing backend is still Ptrace and will only work on x86 and x64 processors. This can be changed to the llvm coverage instrumentation with engine llvm, for Mac and Windows this is the default collection method. It can also be run in Docker, which is useful for when you don't use Linux but want to run it locally.

Kcov is a FreeBSD/Linux/OSX code coverage tester for compiled languages, Python and Bash. Kcov was originally a fork of Bcov, but has since evolved to support a large feature set in addition to that of Bcov. Kcov, like Bcov, uses DWARF debugging information for compiled programs to make it possible to collect coverage information without special compiler switches.

This plugin produces coverage reports. Compared to just using coverage run this plugin does some extras. Subprocess support, so you can fork or run stuff in a subprocess and will get covered without any fuss. Xdist support, so you can use all of pytest-xdist’s features and still get coverage. Consistent pytest behavior. All features offered by the coverage package should work, either through pytest-cov’s command line options or through coverage’s config file. Under certain scenarios, a stray .pth file may be left around in site packages. The data file is erased at the beginning of testing to ensure clean data for each test run. If you need to combine the coverage of several test runs you can use the --cov-append option to append this coverage data to coverage data from previous test runs. The data file is left at the end of testing so that it is possible to use normal coverage tools to examine it.

Early is an AI-driven tool designed to automate the generation and maintenance of unit tests, enhancing code quality and accelerating development processes. By integrating with Visual Studio Code (VSCode), Early enables developers to produce verified and validated unit tests directly from their codebase, covering a wide range of scenarios, including happy paths and edge cases. This approach not only increases code coverage but also helps identify potential issues early in the development cycle. Early supports TypeScript, JavaScript, and Python languages, and is compatible with testing frameworks such as Jest and Mocha. The tool offers a seamless experience by allowing users to quickly access and refine generated tests to meet specific requirements. By automating the testing process, Early aims to reduce the impact of bugs, prevent code regressions, and boost development velocity, ultimately leading to the release of higher-quality software products.

Codacy is an automated code review tool that helps identify issues through static code analysis, allowing engineering teams to save time in code reviews and tackle technical debt. Codacy integrates seamlessly into existing workflows on your Git provider, and also with Slack, JIRA, or using Webhooks. Users receive notifications on security issues, code coverage, code duplication, and code complexity in every commit and pull request along with advanced code metrics on the health of a project and team performance. The Codacy CLI enables running Codacy code analysis locally, so teams can see Codacy results without having to check their Git provider or the Codacy app. Codacy supports more than 30 coding languages and is available in free open-source, and enterprise versions (cloud and self-hosted). For more see https://www.codacy.com/

Coverage.py is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not. Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not. Use coverage run to run your test suite and gather data. However you normally run your test suite, and you can run your test runner under coverage. If your test runner command starts with “python”, just replace the initial “python” with “coverage run”. To limit coverage measurement to code in the current directory, and also find files that weren’t executed at all, add the source argument to your coverage command line. By default, it will measure line (statement) coverage. It can also measure branch coverage. It can tell you what tests ran which lines.

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.

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.