Best Code Coverage Tools for JSON

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 by JetBrains is an IDE for professional Java and Kotlin development. It unlocks productivity and helps you write high quality code with ease. It is designed to get the job finished. It provides all the essential tools and support for cutting-edge technologies you need. It lets you code with ease and confidence thanks to a comfortable, smooth workflow and a strong emphasis on privacy and security.

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/

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.

PHPUnit needs the dom, json extensions. These extensions are usually enabled by default. PHPUnit also needs the reflection, pcre and spl extensions. These extensions are default enabled and cannot be disabled without patching PHP’s build system or C sources. To use the code coverage report feature, you will need to have the Xdebug (2.7.0 and later) and tokenizer extensions. The xmlwriter extension is required to generate XML reports. Unit tests are intended to be a good practice for developers to identify and fix bugs, refactor code, and serve as documentation for the unit of software being tested. Unit tests should cover all possible paths within a program to reap these benefits. A unit test typically covers one path within a function or method. A test method is not always an independent, encapsulated entity. There are often implicit dependencies between test method, which are hidden in the test's implementation scenario.

This module provides Perl code coverage metrics. This module provides code coverage metrics for Perl. They show how thoroughly tests use code. Devel::Cover allows you to identify areas of code that are not being used by your tests. You can also determine which tests to add to increase coverage. Code coverage can be considered an indirect indicator of quality. Devel::Cover has many of the features you would expect in a useful coverage tool. Reports include information on the coverage of statements, branches, conditions, subroutines, and pods. Subroutine and statement coverage data should be accurate. Although not always as accurate as one might expect, branch and condition coverage data should be generally accurate. Pod coverage is derived from Pod::Coverage. If Pod::Coverage::CountParents is available it will be used instead.

grcov aggregates code coverage information from multiple source files. grcov processes.profraw files and.gcda file which can be generated using llvm/clang/gcc. grcov can also process lcov files (for JS cover) and JaCoCo (for Java coverage). Supported operating systems include Windows, macOS, and Linux.

Coverage allows Dart to collect, manipulate, and format coverage data. Collect_coverage stores coverage JSON from Dart VM Service. format_coverage formats JSON coverage data in either LCOV format or pretty-printed format.

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.

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.

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.

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.

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.