Page 2 | Best Application Development Software for Codecov

Rust

Rust is blazingly fast and memory-efficient: with no runtime or garbage collector, it can power performance-critical services, run on embedded devices, and easily integrate with other languages. Rust’s rich type system and ownership model guarantee memory-safety and thread-safety — enabling you to eliminate many classes of bugs at compile-time. Rust has great documentation, a friendly compiler with useful error messages, and top-notch tooling — an integrated package manager and build tool, smart multi-editor support with auto-completion and type inspections, an auto-formatter, and more. Whip up a CLI tool quickly with Rust’s robust ecosystem. Rust helps you maintain your app with confidence and distribute it with ease. Use Rust to supercharge your JavaScript, one module at a time. Publish to npm, bundle with webpack, and you’re off to the races.

Starting Price: Free

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Go

Golang

With a strong ecosystem of tools and APIs on major cloud providers, it is easier than ever to build services with Go. With popular open source packages and a robust standard library, use Go to create fast and elegant CLIs. With enhanced memory performance and support for several IDEs, Go powers fast and scalable web applications. With fast build times, lean syntax, an automatic formatter and doc generator, Go is built to support both DevOps and SRE. Everything there is to know about Go. Get started on a new project or brush up for your existing Go code. An interactive introduction to Go in three sections. Each section concludes with a few exercises so you can practice what you've learned. The Playground allows anyone with a web browser to write Go code that we immediately compile, link, and run on our servers.

Starting Price: Free

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Apache Groovy

The Apache Software Foundation

Apache Groovy is a powerful, optionally typed and dynamic language, with static-typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming. Concise, readable and expressive syntax, easy to learn for Java developers. Closures, builders, runtime & compile-time meta-programming, functional programming, type inference, and static compilation. Flexible & malleable syntax, advanced integration & customization mechanisms, to integrate readable business rules in your applications. Great for writing concise and maintainable tests, and for all your build and automation tasks.

Starting Price: Free

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Julia

Julia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM. Julia uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns. The talk on the Unreasonable Effectiveness of Multiple Dispatch explains why it works so well. Julia is dynamically typed, feels like a scripting language, and has good support for interactive use. Julia provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager, and more. One can build entire Applications and Microservices in Julia. Julia is an open source project with over 1,000 contributors. It is made available under the MIT license.

Starting Price: Free

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Lua

Lua Language

Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description. Lua combines simple procedural syntax with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, runs by interpreting bytecode with a register-based virtual machine, and has automatic memory management with incremental garbage collection, making it ideal for configuration, scripting, and rapid prototyping. Lua has a deserved reputation for performance. To claim to be "as fast as Lua" is an aspiration of other scripting languages. Several benchmarks show Lua as the fastest language in the realm of interpreted scripting languages. Lua is fast not only in fine-tuned benchmark programs, but in real life too. Substantial fractions of large applications have been written in Lua.

Starting Price: Free

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Erlang

Erlang is a programming language used to build massively scalable soft real-time systems with requirements on high availability. Some of its uses are in telecoms, banking, e-commerce, computer telephony and instant messaging. Erlang's runtime system has built-in support for concurrency, distribution and fault tolerance. OTP is set of Erlang libraries and design principles providing middle-ware to develop these systems. It includes its own distributed database, applications to interface towards other languages, debugging and release handling tools.

Starting Price: Free

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Vala

The GNOME Project

Vala is a programming language using modern high level abstractions without imposing additional runtime requirements and without using a different ABI compared to applications and libraries written in C. Vala uses the GObject type system and has additional code generation routines that make targeting the GNOME stack simple. Vala has many other uses where native binaries are required. More. There is GNOME Discourse for developer and general discussions. You can also join the Vala Matrix channel for asking questions and talking with the developers. Vala is a cross platform development tool with third party distributions providing binaries for Windows, macOS, Linux, BSDs and other platforms.

Starting Price: Free

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F#

F# gives you simplicity and succinctness like Python with correctness, robustness and performance beyond C# or Java. F# is open source, cross-platform and free to use with professional tooling. F# is a JavaScript and .NET language for web, cloud, data-science, apps and more. Cloud computing relies on leveraging multiple integrated services. Using multiple services required a unique set of technologies and capabilities, and F# excels in this domain. With the recent rise of cloud solutions, it is becoming increasingly easy to deploy multiple services “in the cloud”, expanding what is possible both by storing large amounts of data and running heavy computations distributed across clusters of machines.

Starting Price: Free

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PHPUnit

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.

Starting Price: Free

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unittest

Python

The unittest unit testing framework was originally inspired by JUnit and has a similar flavor as major unit testing frameworks in other languages. It supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework. A test fixture represents the preparation needed to perform one or more tests, and any associated cleanup actions. This may involve, for example, creating temporary or proxy databases, directories, or starting a server process. A test suite is a collection of test cases, test suites, or both. It is used to aggregate tests that should be executed together. A test runner is a component which orchestrates the execution of tests and provides the outcome to the user. The runner may use a graphical interface, a textual interface, or return a special value to indicate the results of executing the tests.

Starting Price: Free

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Elixir

Elixir is a dynamic, functional language for building scalable and maintainable applications. Elixir leverages the Erlang VM, known for running low-latency, distributed, and fault-tolerant systems. Elixir is successfully used in web development, embedded software, data ingestion, and multimedia processing, across a wide range of industries. Check our getting started guide and our learning page to begin your journey with Elixir. All Elixir code runs inside lightweight threads of execution (called processes) that are isolated and exchange information via messages. Due to their lightweight nature, it is not uncommon to have hundreds of thousands of processes running concurrently in the same machine. Isolation allows processes to be garbage collected independently, reducing system-wide pauses, and using all machine resources as efficiently as possible (vertical scaling). Processes are also able to communicate with other processes running on different machines in the same network.

Starting Price: Free

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Devel::Cover

metacpan

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.

Starting Price: Free

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LuaCov

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.

Starting Price: Free

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Tarpaulin

Tarpaulin is a code coverage reporting tool for the cargo build system, named for a waterproof cloth used to cover cargo on a ship. Currently, tarpaulin provides working line coverage and while fairly reliable may still contain minor inaccuracies in the results. A lot of work has been done to get it working on a wide range of projects, but often unique combinations of packages and build features can cause issues so please report anything you find that's wrong. Also, check out our roadmap for planned features. On Linux Tarpaulin's default tracing backend is still Ptrace and will only work on x86 and x64 processors. This can be changed to the llvm coverage instrumentation with engine llvm, for Mac and Windows this is the default collection method. It can also be run in Docker, which is useful for when you don't use Linux but want to run it locally.

Starting Price: Free

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grcov

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.

Starting Price: Free

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kcov

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.

Starting Price: Free

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test_coverage

pub.dev

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.

Starting Price: Free

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coverage

pub.dev

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.

Starting Price: Free

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cloverage

Cloverage uses clojure.test by default. If you prefer use midje, pass the --runner :midje flag. (In older versions of Cloverage, you had to wrap your midje tests in clojure.test's deftest. This is no longer necessary.) For using eftest, pass the --runner :eftest flag. Optionally you could configure a runner passing :runner-opts with a map in project settings. Other test libraries may ship with their own support for Cloverage external to this library; see their documentation for details.

Starting Price: Free

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Slather

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.

Starting Price: Free

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NCover

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.

Starting Price: Free

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JaCoCo

EclEmma

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.

Starting Price: Free

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OpenClover

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.

Starting Price: Free

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SimpleCov

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.

Starting Price: Free

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DeepCover

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.

Starting Price: Free

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pytest-cov

Python

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.

Starting Price: Free

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XCTest

Apple

Create and run unit tests, performance tests, and UI tests for your Xcode project. Use the XCTest framework to write unit tests for your Xcode projects that integrate seamlessly with Xcode's testing workflow. Tests assert that certain conditions are satisfied during code execution, and record test failures (with optional messages) if those conditions aren’t satisfied. Tests can also measure the performance of blocks of code to check for performance regressions and can interact with an application's UI to validate user interaction flows. A test method is a small, self-contained method that tests a specific part of your code. A test case is a group of related test methods. Add test cases and test methods to a test target to confirm that your code performs as expected. The primary class for defining test cases, test methods, and performance tests. An abstract base class for creating, managing, and executing tests.

Starting Price: Free

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HUnit

Hackage

HUnit is a unit testing framework for Haskell, inspired by the JUnit tool for Java. It's easy to use HUnit, assuming you are familiar with Haskell, though not necessarily with JUnit. A test-centered methodology for software development is most effective when tests are easy to create, change, and execute. The JUnit tool pioneered support for test-first development in Java. HUnit is an adaptation of JUnit to Haskell, a general-purpose, purely functional programming language. With HUnit, as with JUnit, you can easily create tests, name them, group them into suites, and execute them, with the framework checking the results automatically. Test specification in HUnit is even more concise and flexible than in JUnit, thanks to the nature of the Haskell language. HUnit currently includes only a text-based test controller, but the framework is designed for easy extension. Run the tests as a group.

Starting Price: Free

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Pester

Pester is the ubiquitous test and mock framework for PowerShell. Adding Pester tests to Powershell code will enhance code quality and allows you to start creating predictable changes. Visual Studio Code comes with full support for Pester allowing you to create tests quickly. Pester integrates nicely with TFS, Azure, Github, Jenkins, and other CI servers, allowing you to fully automate your development lifecycle. Pester provides a framework for writing and running tests. Pester is most commonly used for writing unit and integration tests, but it is not limited to just that. It is also a base for tools that validate whole environments, computer deployments, database configurations, and so on. Pester tests can execute any command or script that is accessible to a Pester test file. This includes functions, Cmdlets, Modules, and scripts. Pester can be run locally, where it integrates well with Visual Studio Code, and it can of course be integrated into a build script in a CI pipeline.

Starting Price: Free

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Xdebug

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.

Starting Price: Free

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Best Application Development Software for Codecov - Page 2

Compare the Top Application Development Software that integrates with Codecov as of October 2025 - Page 2

Rust