Best Code Coverage Tools for Mac of 2025 - Page 2

The jscoverage tool offers support for both Node.js and JavaScript, allowing for an expanded coverage range. To utilize it, you can load the jscoverage module using Mocha, which enables it to function effectively. When you select different reporters like list, spec, or tap in Mocha, jscoverage will append the coverage information accordingly. You can designate the reporter type using covout, which allows options such as HTML and detailed reporting. The detailed reporter specifically outputs any uncovered code directly to the console for immediate visibility. As Mocha executes test cases with the jscoverage module integrated, it ensures that any files listed in the covignore file are excluded from coverage tracking. Additionally, jscoverage generates an HTML report, providing a comprehensive view of the coverage results. By default, it looks for the covignore file in the root of your project, and it will also copy any excluded files from the source directory to the specified destination directory, ensuring a clean and organized setup for testing. This functionality enhances the testing process by clearly indicating which parts of your code are adequately covered and which areas require further attention.

SimpleCov is a Ruby tool designed for code coverage analysis, leveraging Ruby's native Coverage library to collect data, while offering a user-friendly API that simplifies the processing of results by allowing you to filter, group, merge, format, and display them effectively. Although it excels in tracking the covered Ruby code, it does not support coverage for popular templating systems like erb, slim, and haml. For most projects, obtaining a comprehensive overview of coverage results across various types of tests, including Cucumber features, is essential. SimpleCov simplifies this task by automatically caching and merging results for report generation, ensuring that your final report reflects coverage from all your test suites, thus providing a clearer picture of any areas that need improvement. It is important to ensure that SimpleCov is executed in the same process as the code for which you wish to analyze coverage, as this is crucial for accurate results. Additionally, utilizing SimpleCov can significantly enhance your development workflow by identifying untested code segments, ultimately leading to more robust applications.

Enhance your Ruby testing and GitHub experience with actionable coverage insights that allow your team to deliver robust code efficiently while minimizing the time spent on pull request assessments. Rather than striving for a perfect 100% test coverage, focus on decreasing defects in your pull requests by identifying untested code changes before they go live. After a straightforward setup where the CI server runs tests and sends coverage results to UndercoverCI, you can ensure that every pull request is meticulously examined; we analyze the changes in your code and assess local test coverage for each modified class, method, and block, as merely knowing the overall percentage is insufficient. This tool uncovers untested methods and blocks, highlights unused code paths, and aids in refining your test suite. You can easily integrate UndercoverCI's hosted GitHub App or dive into the array of Ruby gems available. With a fully-featured integration for code review through GitHub, setup is quick and tailored for your organization’s needs. Moreover, the UndercoverCI initiative and its associated Ruby gems are completely open-source and can be utilized freely in your local environment and throughout your CI/CD processes, making it a versatile choice for any development team. By adopting UndercoverCI, you not only improve your code quality but also foster a culture of continuous improvement within your team.

Deep Cover strives to be the premier tool for Ruby code coverage, delivering enhanced accuracy for both line and branch coverage metrics. It serves as a seamless alternative to the standard Coverage library, providing a clearer picture of code execution. A line is deemed covered only when it has been fully executed, and the optional branch coverage feature identifies any branches that remain untraveled. The MRI implementation considers all methods available, including those created through constructs like define_method and class_eval. Unlike Istanbul's method, DeepCover encompasses all defined methods and blocks when reporting coverage. Although loops are not classified as branches within DeepCover, accommodating them can be easily arranged if necessary. Even once DeepCover is activated and set up, it requires only a minimal amount of code loading, with coverage tracking starting later in the process. To facilitate an easy migration for projects that have previously relied on the built-in Coverage library, DeepCover can integrate itself into existing setups, ensuring a smooth transition for developers seeking improved coverage analysis. This capability makes DeepCover not only versatile but also user-friendly for teams looking to enhance their testing frameworks.

This plugin generates detailed coverage reports that offer more functionality compared to merely using coverage run. It includes support for subprocess execution, allowing you to fork or run tasks in a subprocess while still obtaining coverage seamlessly. Additionally, it integrates with xdist, enabling the use of all pytest-xdist features without sacrificing coverage reporting. The plugin maintains consistent behavior with pytest, ensuring that all functionalities provided by the coverage package are accessible either via pytest-cov's command line options or through coverage's configuration file. In rare cases, a stray .pth file might remain in the site packages after execution. To guarantee that each test run starts with clean data, the data file is cleared at the start of testing. If you wish to merge coverage results from multiple test runs, you can utilize the --cov-append option to add this data to that of previous runs. Furthermore, the data file is retained at the conclusion of testing, allowing users to leverage standard coverage tools for further analysis of the results. This additional functionality enhances the overall user experience by providing better management of coverage data throughout the testing process.

Xdebug is a powerful PHP extension that enhances the development workflow by offering various tools and functionalities. It allows developers to step through code in their integrated development environment as scripts run, making debugging much easier. The extension provides an enhanced version of the var_dump() function and delivers stack traces for notices, warnings, errors, and exceptions, clearly indicating the path leading to issues. Additionally, it logs all function calls, including arguments and their locations, to the disk, and can be configured to also record every variable assignment and return value for each function. This feature set enables developers, with the aid of visualization tools, to thoroughly examine the performance of their PHP applications and identify any bottlenecks. Moreover, Xdebug reveals the sections of code that are executed during unit testing with PHPUnit, aiding in better test coverage. For convenience, installing Xdebug via a package manager is typically the quickest method; simply replace the PHP version with the version you are currently using. You can also install Xdebug using PECL on both Linux and macOS, utilizing Homebrew for a streamlined setup process. Overall, Xdebug significantly enhances PHP development by providing essential debugging tools and performance insights.

OpenCppCoverage is a free and open-source tool designed for measuring code coverage in C++ applications on Windows platforms. Primarily aimed at enhancing unit testing, it also aids in identifying executed lines during program debugging. The tool is compatible with compilers that generate program database files (.pdb) and allows users to execute their programs without the need for recompilation. Users can exclude specific lines based on regular expressions, and it offers coverage aggregation, enabling the merging of multiple coverage reports into a singular comprehensive document. It requires Microsoft Visual Studio 2008 or newer, including the Express edition, although it may also function with earlier versions of Visual Studio. Furthermore, tests can be conveniently run through the Test Explorer window, streamlining the testing process for developers. This versatility makes OpenCppCoverage a valuable asset for those focused on maintaining high code quality.

A standalone driver compatible with CodeCoverage for PHP is known as PCOV. When PCOV is not configured, it will search for directories named src, lib, or app in the current working directory sequentially; if none of these are located, it defaults to using the current directory, which can lead to inefficient use of resources by storing coverage data for the entire test suite. If the PCOV configuration includes test code, it is advisable to utilize the exclude command to optimize resource usage. To prevent the unnecessary allocation of additional memory arenas for traces and control flow graphs, PCOV should be adjusted based on the memory demands of the test suite. Furthermore, to avoid table reallocations, the PCOV setting should exceed the total number of files being tested, including all test files. It's important to note that interoperability with Xdebug is not achievable. Internally, PCOV overrides the executor function, which can disrupt any extension or SAPI that attempts to do the same. Notably, PCOV operates at zero cost, allowing code to execute at full speed, thus enhancing performance without additional overhead. This efficiency makes it a valuable tool for developers looking to maintain high performance while ensuring effective code coverage.

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/

Appvance IQ (AIQ), delivers transformative productivity gains and lower costs for both test creation and execution. It offers both AI-driven (fully automated tests) and 3rd-generation codeless scripting for test creation. These scripts are then executed using data-driven functional and performance, app-pen, and API testing -- both for web and mobile apps. AIQ's self healing technology allows you to cover all code with only 10% of the effort required by traditional testing systems. AIQ detects important bugs automatically and with minimal effort. No programming, scripting, logs, or recording are required. AIQ can be easily integrated with your existing DevOps tools, processes, and tools.

dotCover is a powerful code coverage and unit testing tool designed for .NET that seamlessly integrates into Visual Studio and JetBrains Rider. This tool allows developers to assess the extent of their code's unit test coverage while offering intuitive visualization features and is compatible with Continuous Integration systems. It effectively calculates and reports statement-level code coverage for various platforms including .NET Framework, .NET Core, and Mono for Unity. As a plug-in to popular IDEs, dotCover enables users to analyze and visualize coverage directly within their coding environment, facilitating the execution of unit tests and the review of coverage outcomes without having to switch contexts. Additionally, it boasts support for customizable color themes, new icons, and an updated menu interface. Bundled with a unit test runner shared with ReSharper, another JetBrains product for .NET developers, dotCover enhances the testing experience. It also supports continuous testing, allowing it to dynamically identify which unit tests are impacted by code modifications as they occur. This real-time analysis ensures that developers can maintain high code quality throughout the development process.

The LDRA tool suite stands as the premier platform offered by LDRA, providing a versatile and adaptable framework for integrating quality into software development from the initial requirements phase all the way through to deployment. This suite encompasses a broad range of functionalities, which include requirements traceability, management of tests, adherence to coding standards, evaluation of code quality, analysis of code coverage, and both data-flow and control-flow assessments, along with unit, integration, and target testing, as well as support for certification and regulatory compliance. The primary components of this suite are offered in multiple configurations to meet various software development demands. Additionally, a wide array of supplementary features is available to customize the solution for any specific project. At the core of the suite, LDRA Testbed paired with TBvision offers a robust combination of static and dynamic analysis capabilities, along with a visualization tool that simplifies the process of understanding and navigating the intricacies of standards compliance, quality metrics, and analyses of code coverage. This comprehensive toolset not only enhances software quality but also streamlines the development process for teams aiming for excellence in their projects.

Testwell CTC++ is an advanced tool that focuses on instrumentation-based code coverage and dynamic analysis specifically for C and C++ programming languages. By incorporating additional components, it can also extend its functionality to languages such as C#, Java, and Objective-C. Moreover, with further add-ons, CTC++ is capable of analyzing code on a wide range of embedded target machines, including those with very limited resources, such as minimal memory and lacking an operating system. This tool offers various coverage metrics, including Line Coverage, Statement Coverage, Function Coverage, Decision Coverage, Multicondition Coverage, Modified Condition/Decision Coverage (MC/DC), and Condition Coverage. As a dynamic analysis tool, it provides detailed execution counters, indicating how many times each part of the code is executed, which goes beyond simple executed/not executed data. Additionally, users can utilize CTC++ to assess function execution costs, typically in terms of time taken, and to activate tracing for function entry and exit during testing phases. The user-friendly interface of CTC++ makes it accessible for developers seeking efficient analysis solutions. Its versatility and comprehensive features make it a valuable asset for both small and large projects.

Cobertura is an open-source tool for Java that measures how much of your code is tested, helping to pinpoint areas in your Java application that may not have sufficient test coverage. This tool is derived from jcoverage and is offered at no cost. The majority of its components are licensed under the GNU General Public License, which permits users to redistribute and modify the software in accordance with the terms set forth by the Free Software Foundation, specifically under version 2 of the License or any subsequent version you choose. For additional information, it is advisable to consult the LICENSE.txt file included in the distribution package, which provides more detailed guidance on the licensing terms. By utilizing Cobertura, developers can ensure a more robust testing strategy and enhance the overall quality of their Java applications.

Gcov is a tool that provides open-source capabilities for measuring code coverage. It helps developers analyze which parts of their code are executed during testing, allowing for better optimization and debugging.

BullseyeCoverage is an innovative tool designed for C++ code coverage that aims to enhance the quality of software in critical sectors such as enterprise applications, industrial automation, healthcare, automotive, telecommunications, and the aerospace and defense industries. The function coverage metric allows developers to quickly assess the extent of testing and highlights regions that lack coverage entirely. This metric is invaluable for enhancing overall coverage across various facets of your project. On a more granular level, condition/decision coverage offers insights into the control structure, enabling targeted improvements in specific areas, particularly during unit tests. Compared to statement or branch coverage, C/D coverage delivers superior detail and significantly boosts productivity, making it a more effective choice for developers striving for thorough testing. By incorporating these metrics, teams can ensure their software is robust and reliable, meeting the high standards required in critical applications.

Coverlet functions with the .NET Framework on Windows and with .NET Core across all compatible platforms. It provides coverage specifically for deterministic builds. Currently, the existing solution is less than ideal and requires a workaround. For those who wish to view Coverlet's output within Visual Studio while coding, various add-ins are available depending on the platform in use. Additionally, Coverlet seamlessly connects with the build system to execute code coverage post-testing. Activating code coverage is straightforward; you simply need to set the CollectCoverage property to true. To use the Coverlet tool, you must indicate the path to the assembly housing the unit tests. Furthermore, you are required to define both the test runner and the associated arguments by utilizing the --target and --targetargs options. It's crucial that the invocation of the test runner with these arguments does not necessitate recompiling the unit test assembly, as this would prevent the generation of coverage results. Proper configuration and understanding of these aspects will ensure a smoother experience when using Coverlet for code coverage.

Coverage.py serves as a powerful utility for assessing the code coverage of Python applications. It tracks the execution of your program, recording which segments of the code have been activated, and subsequently reviews the source to pinpoint areas that could have been executed yet remained inactive. This measurement of coverage is primarily utilized to evaluate the efficacy of testing efforts. It provides insights into which portions of your code are being tested and which are left untested. To collect data, you can use the command `coverage run` to execute your test suite. Regardless of how you typically run your tests, you can incorporate coverage by executing your test runner with the coverage tool. If the command for your test runner begins with "python," simply substitute the initial "python" with "coverage run." To restrict coverage evaluation to only the code within the current directory and to identify files that have not been executed at all, include the source parameter in your coverage command. By default, Coverage.py measures line coverage, but it is also capable of assessing branch coverage. Additionally, it provides information on which specific tests executed particular lines of code, enhancing your understanding of test effectiveness. This comprehensive approach to coverage analysis can significantly improve the quality and reliability of your codebase.

We assist you in confidently delivering your code by identifying which sections are left untested by your suite. Our service is free for open-source projects, while private repositories can benefit from our pro accounts. You can sign up instantly through platforms like GitHub, Bitbucket, and GitLab. Ensuring a thoroughly tested codebase is crucial for success, yet identifying gaps in your tests can be a challenging task. Since you're likely already using a continuous integration server for testing, why not allow it to handle the heavy lifting? Coveralls integrates seamlessly with your CI server, analyzing your coverage data to uncover hidden issues before they escalate into bigger problems. If you're only checking your code coverage locally, you may miss out on valuable insights and trends throughout your entire development process. Coveralls empowers you to explore every aspect of your coverage while providing unlimited historical data. By using Coveralls, you can eliminate the hassle of monitoring your code coverage, gaining a clear understanding of your untested sections. This allows you to develop with assurance that your code is properly covered and robust. In summary, Coveralls not only streamlines the tracking process but also enhances your overall development experience.

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.