Best Code Coverage Tools for Windows of 2025

BullseyeCoverage, an advanced C++ code cover tool, is used to improve software quality in vital systems like enterprise applications, industrial control and communications. Function coverage gives you an overview of testing completion and highlights areas that are not covered. This metric can be used to increase coverage in all areas of your project. Condition/decision coverage provides details at the control structure level. This metric can be used to achieve high coverage in certain areas, such as unit testing. C/D coverage is more detailed than branch coverage or statement coverage and can be used to achieve higher productivity than more complex coverage metrics.

It works with.NET Framework for Windows and.NET Core for all supported platforms. Coverlet supports coverage for deterministic builds. Unfortunately, the solution is not ideal and requires a workaround. Depending on your platform, the following addins can be used to visualize coverlet output in Visual Studio. Coverlet integrates with the build system for code coverage after tests. It is as easy as setting the CollectCoverage property true to enable code coverage. The path to the assembly containing the unit tests is what invokes the coverlet tool. Also, specify the test runner and arguments to pass to it using the --targetargs and --targetargs options. If you provide the arguments, the invocation of test runner must not result in a recompilation or deletion of the unit test assembly.

NCover Desktop is a Windows program that allows you to collect code coverage statistics for.NET services and applications. Desktop displays coverage metrics and charts in a browser-based GUI. This allows you to drill down to individual lines of source code. Bolt, a Visual Studio extension, is also available in Desktop. Bolt provides code coverage with built-in timings, timings, and source code highlighting directly within Visual Studio IDE. NCover Desktop is a significant leap forward in code coverage tools flexibility and ease. The NCover user can track the code coverage while they test their.NET code and get a precise measurement of the unit test coverage. These statistics can be tracked over time to give you a concrete measure of code quality throughout the development cycle.

JaCoCo is a Java code coverage library that is free and open-source. It was created by the EclEmma group based on lessons learned from integrating libraries over many years. The master branch of JaCoCo can be automatically built and published. Every build is considered fully functional due to the test-driven development approach. For the most recent features and bug fixes, see the change history. SonarCloud.io provides SonarQube code quality metrics for the current JaCoCo implementation. Integrate JaCoCoCo technology into your tools. JaCoCoCo tools are available right out of the box. You can improve the implementation or add new features. There are many open-source Java coverage technologies. The Eclipse plug-in EclEmma was implemented. It was clear that none of them were designed for integration. They are usually only compatible with a specific tool (Ant Tasks, Command Line, IDE Plug-in), and don't offer an API that allows embedding into different contexts.

You should balance your time writing applications with testing code. The most advanced code coverage tool for Java or Groovy. OpenClover measures Java and Groovy code coverage and collects more than 20 code metrics. OpenClover not only shows you the untested areas of your app, but also combines coverage metrics to identify the most risky code. The Test Optimization feature shows you which test cases are related each class of your code. This feature allows OpenClover to run tests that are relevant to changes in your application code. This greatly reduces test execution time. Are testing getters and setters of value? Or machine-generated code. OpenClover is more flexible than other tools when it comes to defining the coverage measurement. You can exclude files, files, classes and methods as well as single statements. You can concentrate on testing the most important parts of your code. OpenClover records test results and measures code coverage for each test.

The JCov open source project is used for gathering quality metrics related to the production of test suites. JCov will be open to allow the verification of regression tests being executed in OpenJDK. JCov's main purpose is transparency in test coverage metrics. OpenJDK developers will have the opportunity to use a code-coverage tool that is in 'lock step with Java language and VM development. This is why we promote standard coverage based JCov. JCov, a Java implementation of a code-coverage tool, provides a way to measure and analyze dynamic Java program code coverage. JCov allows you to see uncovered execution paths and method coverage. It can also show the source code of a program annotated with coverage information. JCov is most useful for testing to determine execution paths.

Simple JavaScript test coverage. Istanbul instruments your ES5 JavaScript code with linecounters. This allows you to track how well your unit tests exercise your codebase. The nyc command line-client for Istanbul is compatible with most JavaScript testing frameworks (tap, mocha, AVA etc. First-class support of ES6/ES2015+ using babel-plugin-Istanbul. Support for JavaScript testing frameworks. Use the nyc command line interface to instrument subprocesses. It is easy to add coverage to your mocha test. Simply add the command nyc to your existing test command. The instrument command of nyc can be used to inspect source files in a context other than your unit tests. nyc can show you the Node processes that were spawned while running a test script. By default, nyc uses Istanbul’s text reporter. You can however specify an alternative reporter.

A seamless JavaScript code coverage library. Blanket.js, a JavaScript code coverage tool, is easy to install, use, and understand. Blanket.js is easy to use and can be customized to your specific needs. JavaScript code coverage complements your JavaScript tests by including code coverage statistics (which lines in your source code are covered) Parsing the code with Esprima and Node-falafel and instrumenting the file using code tracking lines. After the tests are completed, connect to hooks in test runner to get the coverage details. Grunt has been made to allow Blanket to be used as a code coverage tool. This allows you to create instrumented copies from physical files instead of live-instrumenting. PhantomJS runs the QUnit-based Blanket report without any input. The console displays the results. Grunt will fail if you don't meet any coverage thresholds.

jscoverage tool with JavaScript and node.js support. Increase the coverage range. The jscoverage module can be loaded using mocha. When you select list, spec, or tap reporter in mocha, jscoverage will add coverage information. To specify the reporter, such as HTML or detail, you can use covout. The detail reporter will display the uncovered code directly in the console. Mocha runs a test case using the jscoverage module. jscoverage will not list files in the covignore directory. jscoverage will produce a report in HTML format. jscoverage will insert a set of functions into your module exports. Default jscoverage will search for covignore within the project root. jscoverage will copy excluded files from the source directory into the destination directory.

SimpleCov is a Ruby code coverage analysis tool. It uses Ruby's built in Coverage library to gather code data. However, it makes processing the results easier by providing an API to filter, group merge, format and display those results. This allows you to create a complete code coverage suite with just a few lines of code. SimpleCov/Coverage track covered ruby codes, but coverage for common templating options like slim, erb, and haml was not possible. You will want to see all coverage results for your projects. This includes all types of tests and Cucumber features. SimpleCov automatically handles this by caching and merging reports when creating them. This ensures that your report includes coverage across all your test suites, and gives you a better view of any gaps. SimpleCov must run in the same process as the code coverage analysis.

Ruby and GitHub: Actionable test coverage Get insights and checks to help your team deliver healthy code. Stop focusing on 100% coverage of test coverage. Reduce defects in pull requests by notifying when the code hasn't been tested before it is deployed to production. The CI server runs tests, and uploads coverage data for UndercoverCI. This is the only post-installation step. We scan the PR diff to verify local coverage for each class, method, or block. An absolute percentage check is not sufficient. Untested methods and blocks are revealed, as well as unused code paths. This will help you improve your test suite. Explore the Ruby gems family or install UndercoverCI's hosted GitHub app. Easy setup for your organization with a fully-featured GitHub App code inspection integration. The UndercoverCI project, and the related Ruby gems, are completely open-source and can be used locally or in your CI/CD workflows.

Deep Cover is designed to provide the best coverage for Ruby code. You can get more precise line coverage and branch coverage. It can be used to replace the built-in Coverage Library. It provides a better picture of your code usage. A line is considered covered if it is executed completely. Optionally, branch coverage can detect if certain branches are not taken. MRI considers all methods defined, even those defined on objects, via define_method, class_eval and others. DeepCover covers all blocks and defs for Istanbul output. DeepCover does not consider loops to be branches but it is easy to support them if necessary. DeepCover needs to be configured and only a small amount of code is loaded. Coverage is not started even though coverage has been started. Deep-cover can be used to inject itself into existing Coverage libraries to make it easier for projects to transition.

This plugin generates coverage reports. This plugin offers more than just coverage. Subprocess support is available so that you can fork and run stuff in subprocesses and get covered without fuss. Xdist support is available so that you can still use all the features of pytest.xdist and still receive coverage. Consistent pytest behavior. All features of the coverage package should work. In some cases, site packages may contain a stray file.pth. To ensure that each test run has clean data, the data file is deleted at the beginning of testing. You can combine coverage from multiple test runs by using the --cov-append option. This will add this coverage data to coverage data of previous test runs. It is possible to examine the data file using normal coverage tools.

Coverage.py can be used to measure the code coverage of Python programs. It monitors your program and notifies you which parts have been executed. Then it analyzes the source code to find code that could have been executed. It is used to measure the effectiveness of tests. It can help you determine which parts of your code are being used by tests and which are not. Coverage run can be used to run your test suite, gather data, and run it again. You can run your test suite as usual, but your test runner can be run under coverage. If your test runner command begins with "python", replace it with "coverage run". To limit the coverage measurement to code in your current directory and to find files that were not executed, add the source argument (to your coverage command line). It will default measure line (or statement) coverage. It can also measure branch coverage. It can also tell you which tests were run on which lines.

Xdebug is a PHP extension that provides a variety of features to enhance the PHP development experience. You can step through your code while the script is running in your editor or IDE. A new var_dump() function that allows you to stack trace your code to highlight warnings, errors and exceptions. Writes every function call, including arguments and invocation location to the disk. Optionally, includes each variable assignment and return value for each of the functions. With the help of visualization tools, you can analyze the performance of your PHP app and identify bottlenecks. This tool allows you to see which parts of your code are executed when PHPUnit runs unit tests. The fastest way to install Xdebug is often with a package manager. You can replace the PHP version you are using with the version that is compatible. Xdebug can be installed via PECL on Linux & macOS using Homebrew.

A self-contained CodeCoverage compatible driver for PHP. PCOV will attempt, if left unset to find src and lib in the current working director in that order. If none are found, the current directory will be used. This could waste resources for storing coverage information. To avoid wasting resources, it is recommended that PCOV contain test code. To avoid unnecessary allocations of arenas for traces or control flow graphs, PCOV should always be set according to the test suite's memory requirements. To avoid reallocations of tables, PCOV should not be set higher than the number files that will be loaded during testing. This includes test files. Interoperability with Xdebug cannot be achieved. The executor function is disabled at an internal level by PCOV. Any extension or SAPI that does the same will also be broken. Code runs at full speed with PCOV, which is free and has no cost.

We help you deliver code confidently. Open-source repositories are free. Pro accounts are available for private repositories. Sign up instantly through Bitbucket and Gitlab. It is vital to maintain a well-tested codebase. It can be difficult to identify the areas where your tests are failing. It's not difficult to find where your tests are lacking. You already run your tests on a continuous-integration server. Coveralls works in conjunction with your CI server to scan your coverage data and find issues before they become a problem. If you are only running your code coverage locally you won't see any changes or trends that occurred during your entire development cycle. Coveralls allows you to view every detail of your coverage, with unlimited history. Coveralls makes it easy to track your code coverage. You can see where you stand with untested code. You can be confident that your code is protected.

DotCover is a.NET code coverage and unit testing tool that works in Visual Studio and JetBrains Rider. It allows you to see code coverage and helps you to know how much code is covered by unit tests. dotCover calculates and reports the statement-level code coverage for applications that target.NET Framework,.NET Core or Mono for Unity. DotCover is a plug in to Visual Studio and JetBrains Rider. It allows you to analyze and visualize code coverage without ever leaving the code editor. You can run unit tests and analyze coverage results directly in the IDEs. There are also support for new icons, color themes, and menus. DotCover is packaged with ReSharper, a JetBrains tool that allows you to run unit tests. dotCover supports continuous test, which is a modern unit-testing workflow that allows dotCover to determine on-the-fly which unit testers are affected by code changes.

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.

The LDRA tool Suite is LDRA’s flagship platform. It provides open and extensible solutions to build quality into software, from requirements through 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. There are many configurations of the core components of this tool suite that can be used to meet common software development needs. You can tailor the solution to any project with a comprehensive range of add-on capabilities. TBvision and LDRA Testbed provide the foundational static/dynamic analysis engine. They also offer a visualization engine that allows users to easily understand and navigate standards compliance, quality metrics and code coverage analyses.

You can save time and money by finding and fixing problems earlier. You can reduce the time and expense of delivering high quality software by avoiding costly and more complex problems later. Ensure that your C# and VB.NET codes comply with a wide variety of safety and security industry standards. This includes the requirement traceability required and the documentation required for verification. Parasoft's C# tool, Parasoft dotTEST automates a wide range of software quality practices to support your C# or VB.NET development activities. Deep code analysis uncovers reliability issues and security problems. Automated compliance reporting, traceability of requirements, code coverage and code coverage are all key factors in achieving compliance for safety-critical industries and security standards.

Testwell CTC++ is a powerful instrumentation-based code coverage and dynamic analysis tool for C and C++ code. CTC++ can also be used on Objective-C, Java, and C# code with certain add-on components. CTC++ can also be used with certain add-ons to analyse code on any embedded target machine, even in very small ones (limited RAM, no operating system). CTC++ provides Line Coverage, Statement Coverage, Function Coverage, Decision Coverage, Multicondition Coverage, Modified Condition/Decision Coverage (MC/DC), Condition Coverage. CTC++ is a dynamic analysis tool that displays the execution counters of the code (how many times it has been executed), i.e. CTC++ provides more information than just executed/not executed information. CTC++ can also be used to measure the execution cost of function functions (normally time) or to allow function entry/exit traceability at test time. CTC++ is simple to use.

Gcov is an open-source tool for code-coverage.