Best Code Coverage Tools for Travis CI

Are bugs slipping through your QA process and frustrating your customers? Catching issues early shouldn’t mean overwhelming your team with time-consuming tests. With MuukTest’s AI-driven platform, growing engineering teams reach 95% end-to-end test coverage in just 3 months, delivering quality at speed. By leveraging AI, our QA experts rapidly design, manage, and maintain comprehensive E2E tests for web, mobile, and API applications on the MuukTest platform. Within 8 weeks, we deliver full regression coverage, followed by exploratory and negative testing to uncover hidden bugs and expand test scenarios. We also proactively identify and address flaky tests and false results to ensure the reliability of your tests. Testing early and often allows you to detect bugs in the early stages of your development lifecycle, reducing the burden of technical debt down the line.

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.

grcov collects and aggregates code coverage information for multiple source files. grcov processes .profraw and .gcda files which can be generated from llvm/clang or gcc. grcov also processes lcov files (for JS coverage) and JaCoCo files (for Java coverage). Linux, macOS and Windows are supported.

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.

Cloverage uses clojure.test by default. If you prefer use midje, pass the --runner :midje flag. (In older versions of Cloverage, you had to wrap your midje tests in clojure.test's deftest. This is no longer necessary.) For using eftest, pass the --runner :eftest flag. Optionally you could configure a runner passing :runner-opts with a map in project settings. Other test libraries may ship with their own support for Cloverage external to this library; see their documentation for details.

Generate test coverage reports for Xcode projects & hook it into CI. Enable test coverage by ticking the "Gather coverage data" checkbox when editing a scheme.

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.

Advanced fuzzing solution that combines guided fuzzing with symbolic execution, a patented technology from CMU. Mayhem is an advanced fuzz testing solution that dramatically reduces manual testing efforts with autonomous defect detection and validation. Deliver safe, secure, reliable software with less time, cost, and effort. Mayhem’s unique advantage is in its ability to acquire intelligence of its targets over time. As Mayhem’s knowledge grows, it deepens its analysis and maximizes its code coverage. All reported vulnerabilities are exploitable, confirmed risks. Mayhem guides remediation efforts with in-depth system level information, such as backtraces, memory logs, and register state, expediting issue diagnosis and fixes. Mayhem utilizes target feedback to custom generate test cases on the fly -- meaning no manual test case generation required. Mayhem offers access to all of its test cases to make regression testing effortless and continuous.

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.