Best Code Coverage Tools for Docker

Parasoft's mission is to provide automated testing solutions and expertise that empower organizations to expedite delivery of safe and reliable software. 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, a JetBrains IDE, is the leading IDE for Java and Kotlin development. It helps you stay productive with a suite of efficiency-enhancing features such as intelligent coding assistance, reliable refactorings, instant code navigation, built-in developer tools, web and enterprise development support, and much more.

Develop healthier code. Improve code quality and workflow. Codecov offers a comprehensive suite of tools that allow you to group, merge and archive coverage reports, as well as compare them. Open source. Plans starting at $10/user per month. Ruby, Python and C++ as well as Javascript are all available. All CI products and workflows can be integrated easily. No setup is required. Automatic report merging for all CI languages and languages into one report. You can get custom statuses for any group of coverage metrics. You can review coverage reports by folder, project, and type (unit tests or integration tests). You can comment the detailed report directly in your pull request. Codecov is SOC 2 Type II Certified. This means that Codecov has been audited by a third party and attested to our security practices.

All Go developers, whether they are newbies or professionals, can use the on-the-fly error identification and suggestions to fix it. One-step undo and intelligent code completion are all available. Documentation hints and dead code detection are also available. It takes a lot of effort and time to understand legacy, team, and foreign projects. GoLand code navigation allows you to quickly switch between shadowed methods, implementations and usages. You can jump between files, types, and other symbols. You can also find their usages, and examine them with a convenient grouping by type. You can run and debug your applications with powerful built-in tools. You can create and debug tests with no additional plugins or configuration work, and you can test your applications directly in the IDE. The IDE includes a built-in Code Coverage tool to ensure that your tests do not miss any important information.

Tarpaulin, a code coverage reporting tool for Cargo build system is named after a waterproof cloth that was used to cover cargo on ships. Tarpaulin currently provides working line coverage, but the results may not be 100% reliable. It has taken a lot of work to get it working on many different projects. However, sometimes unique combinations of packages or build features can cause problems so please report any issues. Check out our roadmap to see the planned features. Linux Tarpaulin's default trace backend is still Ptrace. It will only work with x86_64 processors. This can be changed to llvm coverage instrumentation by using --engine llvm. For Windows and Mac, this is the default collection method. You can also run it in Docker. This is useful for those who don't have Linux but still want to run it locally. during development. For more information, see below.

Codacy is an automated code review tool. It helps identify problems through static code analysis. This allows engineering teams to save time and tackle technical debt. Codacy seamlessly integrates with your existing workflows on Git provider as well as with Slack and JIRA or using Webhooks. Each commit and pull-request includes notifications about security issues, code coverage, duplicate code, and code complexity. Advanced code metrics provide insight into the health of a project as well as team performance and other metrics. The Codacy CLI allows you to run Codacy code analysis locally. This allows teams to see Codacy results without needing to check their Git provider, or the Codacy app. Codacy supports more than 30 programming languages and is available in free open source and enterprise versions (cloud or self-hosted). For more see https://www.codacy.com/

SonarCloud automatically analyzes and decorates pull request branches to maximize your throughput. To prevent undefined behavior from affecting end-users, catch tricky bugs. Security Hotspots will help you identify and fix vulnerabilities that could compromise your app. It takes just a few mouse clicks to get your code up and running. Instant access to the most recent features and enhancements. Project dashboards keep stakeholders and teams informed about code quality and releasability. Show your communities that you care about awesome by displaying project badges. Your entire stack should be concerned about code quality and security. We cover 24 languages, including C++, Java, Python, and many other. Transparency is a good thing and the trend is growing. Join the fun! Open-source projects are completely free!

DeepSource allows you to automatically identify and fix bugs in your code during code reviews. This includes security flaws, anti-patterns and bug risks. It takes less that 5 minutes to create your Bitbucket or GitLab account. It works with Python, Go, Ruby and JavaScript.

Kcov is a FreeBSD/Linux/OSX code cover tester for compiled languages like Python and Bash. Kcov was originally forked from Bcov. However, it has evolved to support a larger feature set than Bcov. Kcov uses DWARF debugging info for compiled programs, just like Bcov. This makes it possible to collect coverage information with no special compiler switches.

Our platform uses a variety of security techniques, including feedback-based fuzz testing and coverage-guided fuzz testing, in order to generate millions upon millions of test cases that trigger difficult-to-find bugs deep in your application. This white-box approach helps to prevent edge cases and speed up development. Advanced fuzzing engines produce inputs that maximize code coverage. Powerful bug detectors check for errors during code execution. Only uncover true vulnerabilities. You will need the stack trace and input to prove that you can reproduce errors reliably every time. AI white-box testing is based on data from all previous tests and can continuously learn the inner workings of your application. This allows you to trigger security-critical bugs with increasing precision.

Maintain high-quality code while adhering to agile development cycles. Jtest's extensive Java testing tools will ensure that you code flawlessly at every stage of Java software development. Streamline Compliance with Security Standards. Ensure that your Java code conforms to industry security standards. Automated generation of compliance verification documentation Get Quality Software Out Faster Java testing tools can be integrated to detect defects faster and more efficiently. Reduce time and costs by avoiding costly and complicated problems later. Increase your return on unit testing. Create a set of JUnit test suites that are easy to maintain and optimize for code coverage. Smart test execution allows you to get faster feedback from CI as well as within your IDE. Parasoft Jtest integrates seamlessly into your development ecosystem and CI/CD pipeline for real-time, intelligent feedback about your testing and compliance progress.

Advanced fuzzing solution that combines guided and symbolic execution. This technology is patented by CMU. Mayhem, an advanced fuzz testing solution, dramatically reduces manual testing with autonomous defect detection. You can deliver reliable, secure software in a shorter timeframe, at a lower cost, and with less effort. Mayhem's unique advantage lies in its ability to continuously acquire intelligence about its targets. Mayhem's knowledge increases and it expands its analysis. This allows it to maximize its code coverage. All vulnerabilities reported are exploitable and confirmed risks. Mayhem provides detailed system-level information such as backtraces, memory logs and register state to assist in remediation efforts. This helps speed up issue diagnosis and fixes. Mayhem uses target feedback to automatically generate test cases -- no need for manual testing. Mayhem provides access to all its test cases, making regression testing easy and continuous.