Tarpaulin Alternatives

Parasoft helps organizations continuously deliver high-quality software with its AI-powered software testing platform and automated test solutions. Supporting embedded and enterprise markets, Parasoft’s proven technologies reduce the time, effort, and cost of delivering secure, reliable, and compliant software by integrating everything from deep code analysis and unit testing to UI and API testing, plus service virtualization and complete code coverage, into the delivery pipeline. 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.

Coco® is a multi-language code coverage tool. Automatic source code instrumentation is used to measure test coverage of statements, branches and conditions. Executing a test suite against an instrumented application produces data that can later be analyzed. This analysis can be used to understand how much of the source code has been hit by tests, which additional tests need to be written, how the test coverage changed over time and more. Identify redundant tests, untested or dead code. Identify the impact of a patch on the code and code coverage & your testing. Coco supports statement coverage, branch coverage, MC/DC and other levels. Linux, Windows, RTOS and others. Using GCC, Visual Studio, embedded compilers and more. Choice of different report formats (text, HTML, XML, JUnit, Cobertura). Coco can also be integrated with various build, test and CI frameworks like JUnit, Jenkins and SonarQube.

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.

JavaScript test coverage made simple. Istanbul instruments your ES5 and ES2015+ JavaScript code with line counters, so that you can track how well your unit-tests exercise your codebase. The nyc command-line-client for Istanbul works well with most JavaScript testing frameworks, tap, mocha, AVA, etc. First-class support of ES6/ES2015+ using babel-plugin-Istanbul. Support for the most popular JavaScript testing frameworks. Support for instrumenting subprocesses, using the nyc command-line interface. Adding coverage to your mocha tests could not be easier. Now, simply place the command nyc in front of your existing test command. nyc's instrument command can be used to instrument source files outside of the context of your unit tests. nyc is able to show you all Node processes that are spawned when running a test script under it. By default, nyc uses Istanbul's text reporter. However, you may specify an alternative reporter.

PHPUnit requires the dom and json extensions, which are normally enabled by default. PHPUnit also requires the pcre, reflection, and spl extensions. These standard extensions are enabled by default and cannot be disabled without patching PHP’s build system and/or C sources. The code coverage report feature requires the Xdebug (2.7.0 or later) and tokenizer extensions. Generating XML reports requires the xmlwriter extension. Unit Tests are primarily written as a good practice to help developers identify and fix bugs, to refactor code and to serve as documentation for a unit of software under test. To achieve these benefits, unit tests ideally should cover all the possible paths in a program. One unit test usually covers one specific path in one function or method. However a test method is not necessarily an encapsulated, independent entity. Often there are implicit dependencies between test methods, hidden in the implementation scenario of a test.

Testwell CTC++ is a powerful instrumentation-based code coverage and dynamic analysis tool for C and C++ code. With certain add-on components CTC++ can be used also on C#, Java and Objective-C code. Further, again with certain add-on components, CTC++ can be used to analyse code basically at any embedded target machines, also in very small ones (limited memory, no operating system). CTC++ provides Line Coverage, Statement Coverage, Function Coverage, Decision Coverage, Multicondition Coverage, Modified Condition/Decision Coverage (MC/DC), Condition Coverage. As a dynamic analysis tool, CTC++ shows the execution counters (how many times executed) in the code, i.e. more than a plain executed/not executed information. You can also use CTC++ to measure function execution costs (normally time) and to enable function entry/exit tracing at test time. CTC++ is easy to use.

OpenCppCoverage is an open-source code coverage tool for C++ under Windows. The main usage is for unit testing coverage, but you can also use it to know the executed lines in a program for debugging purposes. Support compiler with a program database file (.pdb). Just run your program with OpenCppCoverage, no need to recompile your application. Exclude a line based on a regular expression. Coverage aggregation, to run several code coverages and merge them into a single report. Requires Microsoft Visual Studio 2008 or higher for all editions including the Express edition. It should also work with the previous version of Visual Studio. You can run the tests with the Test Explorer window.

This plugin produces coverage reports. Compared to just using coverage run this plugin does some extras. Subprocess support, so you can fork or run stuff in a subprocess and will get covered without any fuss. Xdist support, so you can use all of pytest-xdist’s features and still get coverage. Consistent pytest behavior. All features offered by the coverage package should work, either through pytest-cov’s command line options or through coverage’s config file. Under certain scenarios, a stray .pth file may be left around in site packages. The data file is erased at the beginning of testing to ensure clean data for each test run. If you need to combine the coverage of several test runs you can use the --cov-append option to append this coverage data to coverage data from previous test runs. The data file is left at the end of testing so that it is possible to use normal coverage tools to examine it.

A seamless JavaScript code coverage library. Blanket.js is a code coverage tool for JavaScript that aims to be easy to install, easy to use, and easy to understand. Blanket.js can be run seamlessly or can be customized for your needs. JavaScript code coverage compliments your existing JavaScript tests by adding code coverage statistics (which lines of your source code are covered by your tests). Parsing the code using Esprima and node-falafel, and instrumenting the file by adding code tracking lines. Connecting to hooks in the test runner to output the coverage details after the tests have been completed. A Grunt plugin has been created to allow you to use Blanket like a "traditional" code coverage tool (creating instrumented copies of physical files, as opposed to live-instrumenting). Runs the QUnit-based Blanket report headlessly using PhantomJS. Results are displayed on the console, and the task will cause Grunt to fail if any of your configured coverage thresholds are not met.

It works with .NET Framework on Windows and .NET Core on all supported platforms. Coverlet supports coverage for deterministic builds. The solution at the moment is not optimal and need a workaround. If you want to visualize coverlet output inside Visual Studio while you code, you can use the following addins depending on your platform. Coverlet also integrates with the build system to run code coverage after tests. Enabling code coverage is as simple as setting the CollectCoverage property to true. The coverlet tool is invoked by specifying the path to the assembly that contains the unit tests. You also need to specify the test runner and the arguments to pass to the test runner using the --target and --targetargs options respectively. The invocation of the test runner with the supplied arguments must not involve a recompilation of the unit test assembly or no coverage result will be generated.

jscoverage tool, both node.js and JavaScript support. Enhance the coverage range. Use mocha to load the jscoverage module, then it works. jscoverage will append coverage info when you select list or spec or tap reporter in mocha. You can use covout to specify the reporter, like HTML, and detail. The detail reporter will print the uncovered code in the console directly. Mocha runs test case with jscoverage module. jscoverage will ignore files while listing in covignore file. jscoverage will output a report in HTML format. jscoverage will inject a group of functions into your module exports. default jscoverage will search covignore in the project root. jscoverage will copy exclude files from the source directory to the destination directory.

Automating the creation and deployment of component test harnesses, test stubs and test drivers is a cinch thanks to OneTest Embedded. With a single click from any development environment, one can profile memory and performance, analyze code coverage and visualize program execution behavior. Additionally, OneTest Embedded helps be more proactive in debugging, while identifying and assisting in fixing code before it breaks. Allows for a virtual cycle of test generation, while executing, reviewing and testing improvement to rapidly achieve full test coverage. One click is all it takes to build, execute on the target, and generate reports. Helps preempt performance issues and program crashes. Additionally, can be adapted to work with custom memory management methods used in embedded software. Provides visibility on thread execution and switching to develop a deep understanding of the behavior of the system under test.

Do you want everything set up for you instantly, or do you want to customize your environment and your workflow? CodeShip lets the developer pick the path that’s best for them, to maximize productivity and let teams evolve over time. From deployments to notifications to code coverage to security scanning and on-premise SCMs, CodeShip lets you integrate with any tool, service or cloud you need for your organization’s perfect workflow. Not only do we make CodeShip easy to use, we also provide fast and thorough developer support. When you need help or identify a problem, you want to talk to someone technical sooner rather than later, and that’s what you’ll get with CodeShip. You can get your builds and deployments working in less than 5 minutes with CodeShip’s turnkey environment and simple UI. From there, you can evolve into more sophisticated workflows and config-as-code as your projects grow.

Coverage.py is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not. Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not. Use coverage run to run your test suite and gather data. However you normally run your test suite, and you can run your test runner under coverage. If your test runner command starts with “python”, just replace the initial “python” with “coverage run”. To limit coverage measurement to code in the current directory, and also find files that weren’t executed at all, add the source argument to your coverage command line. By default, it will measure line (statement) coverage. It can also measure branch coverage. It can tell you what tests ran which lines.

LuaCov is a simple coverage analyzer for Lua scripts. When a Lua script is run with the luacov module loaded, it generates a stats file with the number of executions of each line of the script and its loaded modules. The luacov command-line script then processes this file generating a report file which allows one to visualize which code paths were not traversed, which is useful for verifying the effectiveness of a test suite. LuaCov includes several configuration options, which have their defaults stored in src/luacov/defaults.lua. These are the global defaults. To use project specific configuration, create a Lua script setting options as globals or returning a table with some options and store it as .luacov in the project directory from where luacov is being run. For example, this config informs LuaCov that only foo module and its submodules should be covered and that they are located inside src directory.

dotCover is a .NET unit testing and code coverage tool that works right in Visual Studio and in JetBrains Rider, helps you know to what extent your code is covered with unit tests, provides great ways to visualize code coverage, and is Continuous Integration ready. dotCover calculates and reports statement-level code coverage in applications targeting .NET Framework, .NET Core, Mono for Unity, etc. dotCover is a plug-in to Visual Studio and JetBrains Rider, giving you the advantage of analyzing and visualizing code coverage without leaving the code editor. This includes running unit tests and analyzing coverage results right in the IDEs, as well as support for different color themes, new icons and menus. dotCover comes bundled with a unit test runner that it shares with another JetBrains tool for .NET developers, ReSharper. dotCover supports continuous testing, a modern unit testing workflow whereby dotCover figures out on-the-fly which unit tests are affected by your code changes.

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.

A self-contained CodeCoverage compatible driver for PHP. When PCOV is left unset, PCOV will attempt to find src, lib or, app in the current working directory, in that order; If none are found the current directory will be used, which may waste resources storing coverage information for the test suite. If PCOV contains test code, it's recommended to set the exclude command to avoid wasting resources. To avoid unnecessary allocation of additional arenas for traces and control flow graphs, PCOV should be set according to the memory required by the test suite. To avoid reallocation of tables, PCOV should be set to a number higher than the number of files that will be loaded during testing, inclusive of test files. interoperability with Xdebug is not possible. At an internal level, the executor function is overridden by PCOV, so any extension or SAPI which does the same will be broken. PCOV is zero cost, code runs at full speed.

NCrunch tracks your code coverage in real-time, showing this in markers next to your code. This makes it easy to track where your coverage is heavy or light. NCrunch was designed with big complex projects in mind. We've spent the last 12 years optimising and scaling the NCrunch system to meet the needs of real-world systems consisting of millions of lines of code and many thousands of tests. NCrunch tracks all sorts of test related data, and it uses it to give you the most important feedback as fast as possible. Tests that you have recently impacted with your code changes are prioritised for execution using sophisticated and high performance IL-based change mapping. NCrunch can offload build and test work to other computers for processing. Farm tasks out to connected machines or scale into the cloud. Processing resources can be shared between developers allowing teams to pool their testing resources.

Xdebug is an extension for PHP, and provides a range of features to improve the PHP development experience. A way to step through your code in your IDE or editor while the script is executing. An improved var_dump() function, stack traces for notices, warnings, errors, and exceptions to highlight the code path to the error. Writes every function call, with arguments and invocation location to disk. Optionally also includes every variable assignment and return value for each function. Allows you, with the help of visualization tools, to analyze the performance of your PHP application and find bottlenecks. Shows which parts of your code base are executed when running unit tests with PHPUnit. Installing Xdebug with a package manager is often the fastest way. You can substitute the PHP version with the one that matches the PHP version that you are running. You can install Xdebug through PECL on Linux & macOS with Homebrew.

Introducing Xcode 12. With an all-new design that looks great on macOS Big Sur, Xcode 12 has customizable font sizes for the navigator, streamlined code completion, and new document tabs. Xcode 12 builds Universal apps by default to support Mac with Apple Silicon, often without changing a single line of code. Designed for macOS Big Sur. Xcode 12 looks great on macOS Big Sur, with a navigator sidebar that goes to the top of the window and clear new toolbar buttons. The navigator defaults to a larger font that’s easier to read, while giving you multiple size choices. New document tabs make it easy to create a working set of files within your workspace. Document tabs. The new tab model lets you open a new tab with a double-click, or track the selected file as you click around the navigator. You can re-arrange the document tabs to create a working set of files for your current task, and configure how content is shown within each tab.

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.

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.

This module provides code coverage metrics for Perl. Code coverage metrics describe how thoroughly tests exercise code. By using Devel::Cover you can discover areas of code not exercised by your tests and determine which tests to create to increase coverage. Code coverage can be considered an indirect measure of quality. Devel::Cover is now quite stable and provides many of the features to be expected in a useful coverage tool. Statement, branch, condition, subroutine, and pod coverage information is reported. Statement and subroutine coverage data should be accurate. Branch and condition coverage data should be mostly accurate too, although not always what one might initially expect. Pod coverage comes from Pod::Coverage. If Pod::Coverage::CountParents is available it will be used instead.

A simple command-line tool to collect test coverage information from Dart VM tests. It is useful if you need to generate coverage reports locally during development.

The JCov open-source project is used to gather quality metrics associated with the production of test suites. JCov is being opened in order to facilitate the practice of verifying test execution of regression tests in OpenJDK development. The main motivation behind JCov is the transparency of test coverage metrics. The advantage to promoting standard coverage based on JCov is that OpenJDK developers will be able to use a code coverage tool that stays in the 'lock step' with Java language and VM developments. JCov is a pure java implementation of a code coverage tool that provides a means to measure and analyze dynamic code coverage of Java programs. JCov provides functionality to collect method, linear block, and branch coverage, as well as show uncovered execution paths. It is also able to show a program's source code annotated with coverage information. From a testing perspective, JCov is most useful to determine execution paths.

NCover Desktop is a Windows application that helps you collect code coverage statistics for .NET applications and services. After coverage is collected, Desktop displays charts and coverage metrics in a browser-based GUI that allows you to drill all the way down to your individual lines of source code. Desktop also allows you the option to install a Visual Studio extension called Bolt. Bolt offers built-in code coverage that displays unit test results, timings, branch visualization and source code highlighting right in the Visual Studio IDE. NCover Desktop is a major leap forward in the ease and flexibility of code coverage tools. Code coverage, gathered while testing your .NET code, shows the NCover user what code was exercised during the test and gives a specific measurement of unit test coverage. By tracking these statistics over time, you gain a concrete measurement of code quality during the development cycle.

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.

SimpleCov is a code coverage analysis tool for Ruby. It uses Ruby's built-in Coverage library to gather code coverage data, but makes processing its results much easier by providing a clean API to filter, group, merge, format, and display those results, giving you a complete code coverage suite that can be set up with just a couple lines of code. SimpleCov/Coverage track covered ruby code, gathering coverage for common templating solutions like erb, slim, and haml is not supported. In most cases, you'll want overall coverage results for your projects, including all types of tests, Cucumber features, etc. SimpleCov automatically takes care of this by caching and merging results when generating reports, so your report actually includes coverage across your test suites and thereby gives you a better picture of blank spots. SimpleCov must be running in the process that you want the code coverage analysis to happen on.

Deep Cover aims to be the best coverage tool for Ruby code. More accurate line coverage, and branch coverage. It can be used as a drop-in replacement for the built-in Coverage library. It reports a more accurate picture of your code usage. In particular, a line is considered covered if and only if it is entirely executed. Optionally, branch coverage will detect if some branches are never taken. MRI considers every method defined, including methods defined on objects or via define_method, class_eval, etc. For Istanbul output, DeepCover has a different approach and covers all def and all blocks. DeepCover doesn't consider loops to be branches, but it's easy to support them if needed. Even after DeepCover is required and configured, only a very minimal amount of code is actually loaded and coverage is not started. To make it easier to transition for projects already using the builtin Coverage library deep-cover can inject itself into those tools.

Balance your effort spent on writing applications and test code. Use the most sophisticated code coverage tool for Java and Groovy. OpenClover measures code coverage for Java and Groovy and collects over 20 code metrics. It not only shows you untested areas of your application but also combines coverage and metrics to find the riskiest code. The Test Optimization feature tracks which test cases are related to each class of your application code. Thanks to this OpenClover can run tests relevant to changes made in your application code, significantly reducing test execution time. Do testing getters and setters bring much value? Or machine-generated code? OpenClover outruns other tools in its flexibility to define the scope of coverage measurement. You can exclude packages, files, classes, methods, and even single statements. You can focus on testing important parts of your code. OpenClover not only records test results but also measures individual code coverage for every test.

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.

Cobertura is a free Java tool that calculates the percentage of code accessed by tests. It can be used to identify which parts of your Java program are lacking test coverage. It is based on jcoverage. Cobertura is free software. Most of it is licensed under the GNU GPL, and you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Please review the file LICENSE.txt included in this distribution for further details.

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.

Debugging should be simple. AQTime Pro synthesizes complex memory and performance information into digestible, actionable insights so you can quickly find bugs and their root cause. Finding and squashing highly differentiated bugs is tedious and complicated, but AQTime Pro makes it easy. With over a dozen profilers, you can find memory leaks, performance bottlenecks, code coverage gaps and more in just a few clicks. AQTime Pro enables you to squash all bugs with one tool and get back to making high quality code. Don’t let code profilers box you in with a single codebase or framework and prevent you from finding performance bottlenecks, memory leaks, code coverage gaps unique to your project. AQTime Pro is the one tool to use across multiple codebases and frameworks in a project. It has broad language support for C/C++, Delphi, .NET, Java and more.

The easiest way to unit test. Write tests without changing your code! Even legacy code. Static methods, private methods, non-virtual methods, out parameters and even members and fields. Our professional edition is free for developers around the world. We also have paid support package. Improve your code integrity and deliver quality code. Fake entire object models with a single statement. Mock statics, private, constructors, events, linq, ref args, live, future, static constructors. Our suggest feature creates automated test suggestions suitable for your code. Our smart runner will run only your impact tests and get you super fast feedback. Our coverage feature displays your code coverage in your editor while you code.

The LDRA tool suite is LDRA’s flagship platform that delivers open and extensible solutions for building quality into software from requirements through to 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. The core components of the tool suite are available in several configurations that align with common software development needs. A comprehensive set of add-on capabilities are available to tailor the solution for any project. LDRA Testbed together with TBvision provide the foundational static and dynamic analysis engine, and a visualization engine to easily understand and navigate standards compliance, quality metrics, and code coverage analyses.

Mainly, a better-lookin' report UI, a simpler maven configuration. Add the plugin instrumentation settings to child projects and the report merging settings to the parent project.

Save time and money by finding and fixing defects earlier. Reduce the effort and cost of delivering high-quality software by preventing more complicated and expensive problems down the line. Ensure your C# or VB.NET code complies with a wide range of safety and security industry standards, including the requirement traceability mandated and the documentation required to verify compliance. Parasoft's C# testing tool, Parasoft dotTEST, automates a broad range of software quality practices for your C# and VB.NET development activities. Deep code analysis uncovers reliability and security issues. Code coverage, requirements traceability, and automated compliance reporting helps achieve compliance for security standards and safety-critical industries.

JaCoCo is a free code coverage library for Java, which has been created by the EclEmma team based on the lessons learned from using and integrating existing libraries for many years. The master branch of JaCoCo is automatically built and published. Due to the test-driven development approach, every build is considered fully functional. See the change history for the latest features and bug fixes. SonarQube code quality metrics of the current JaCoCo implementation are available on SonarCloud.io. Integrate JaCoCo technology with your tools. Use JaCoCo tools out of the box. Improve the implementation and add new features. There are several open-source coverage technologies for Java available. While implementing the Eclipse plug-in EclEmma the observation was that none of them are really designed for integration. Most of them are specifically fit to a particular tool (Ant tasks, command line, IDE plug-in) and do not offer a documented API that allows embedding in different contexts.

Linux, Windows, RTOS and others. Using gcc, Visual Studio, embedded compilers and more. Merging multiple execution reports to provide advanced analysis and more outstanding features. Assess and optimize code performance with Coco’s built-in Function Profiler.

Codacy is an automated code review tool that helps identify issues through static code analysis, allowing engineering teams to save time in code reviews and tackle technical debt. Codacy integrates seamlessly into existing workflows on your Git provider, and also with Slack, JIRA, or using Webhooks. Users receive notifications on security issues, code coverage, code duplication, and code complexity in every commit and pull request along with advanced code metrics on the health of a project and team performance. The Codacy CLI enables running Codacy code analysis locally, so teams can see Codacy results without having to check their Git provider or the Codacy app. Codacy supports more than 30 coding languages and is available in free open-source, and enterprise versions (cloud and self-hosted). For more see https://www.codacy.com/

BMC Compuware Xpediter is a family of debuggers and interactive analysis tools for COBOL, Assembler, PL/I, and C programs that helps developers quickly understand applications, make changes, and fix problems in a secure environment—even if they’re unfamiliar with the source code. Xpediter enables developers to get into an interactive test session with minimal effort and quickly move applications into production with greater confidence. See line-by-line code execution and control all aspects of program execution and data. Use Code Coverage to see proof of execution and view metrics on multi-platform applications. Access Abend-AID diagnostic capabilities from within a debugging session. See a graphical view of source code through an integration with Topaz for Program Analysis. Leverage Topaz for Total Test for building a comprehensive portfolio of automated virtualized test cases. Intercept and debug mainframe transactions initiated remotely.

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.

Appvance IQ (AIQ) delivers transformational productivity gains and lower costs in both test creation and execution. For test creation, it offers both AI-driven (fully machine-generated tests) and also 3rd-generation, codeless scripting. It then executes those scripts through data-driven functional, performance, app-pen and API testing — for both web and mobile apps. AIQ’s self-healing technology gives you complete code coverage with just 10% the effort of traditional testing systems. Most importantly, AIQ finds important bugs autonomously, with little effort. No coding, scripting, logs or recording required. AIQ is easy to integrate with your current DevOps tools and processes. Appvance IQ was developed by a pioneering team who envisioned a better way to test. Their innovative vision has been made possible by applying differentiated, patented AI methods to test creation while leveraging today’s high-availability compute resources for massive levels of parallel execution.

Coverage provides coverage data collection, manipulation, and formatting for Dart. Collect_coverage collects coverage JSON from the Dart VM Service. format_coverage formats JSON coverage data into either LCOV or pretty-printed format.

Gcov is an open-source code-coverage tool.

BullseyeCoverage is an advanced C++ code coverage tool used to improve the quality of software in vital systems such as enterprise applications, industrial control, medical, automotive, communications, aerospace and defense. The function coverage metric gives you a quick overview of testing completeness and indicates areas with no coverage at all. Use this metric to broadly raise coverage across all areas of your project. Condition/decision coverage provides detail at the control structure level. Use this metric to attain high coverage in specific areas, for example during unit testing. C/D coverage provides better detail than statement coverage or branch coverage, and provides much better productivity than more complex coverage metrics.

Velocity provides in-depth, contextual analytics that equip engineering leaders to support stuck team members, address team roadblocks, and streamline engineering processes. Actionable metrics for engineering leaders. Velocity turns data from commits and pull requests into the insights you need to make lasting improvements to your team’s productivity. Quality: Automated code review for test coverage, maintainability and more so that you can save time and merge with confidence. Receive automated code review comments on your pull requests. Our 10-point technical debt assessment provides real-time feedback, so you can save time and focus on what matters in your code review discussions. Get test coverage right, every time. See coverage line by line within diffs. Never merge code without sufficient tests again. At a glance, identify frequently changed files that have inadequate coverage and maintainability issues. Track your progress against measurable goals, day-by-day.

Actionable test coverage for Ruby and GitHub. Checks and insights to help your team ship healthy code while saving time on PR reviews. Stop focusing on getting to 100% test coverage. Reduce pull request defects by telling when the changed code is untested before it's deployed to production. The CI server runs tests and uploads coverage data to UndercoverCI. That's the only required post-install setup step! We scan the PR diff and verify local test coverage for each updated class, method, and block because an absolute percentage check is not enough. Reveal untested methods and blocks, find unused code paths, and improve your test suite. Install UndercoverCI's hosted GitHub App or explore the Ruby gems family. Fully-featured GitHub App code review integration with quick setup for your organization. The UndercoverCI project and related Ruby gems are entirely open-source and free to use locally and in your CI/CD workflows.