An introduction to the build pipeline, and how we can orchestrate it manually

Before working with CMake, it's useful to understand the tools it orchestrates. This lesson guides you through setting up a C++ toolchain.

Building C++ Programs

Walking through the steps of the C++ compilation process, from source code to machine code.

The Compilation Pipeline

The linker's role in the build process, how it resolves symbols to combine object files, and how to troubleshoot common errors.

Object Files and the Linking Process

The difference between static and dynamic libraries, how to create them, and the trade-offs between them.

Static and Shared Libraries

The C++ Build Process

Recommended approaches for managing large projects and the libraries that they depend on

Learn how to structure your C++ projects for scalability and maintainability, from basic directory layouts to managing multiple modules.

Organizing a C++ Project

Learn to manage third-party libraries in C++, covering include/library paths, static vs. shared libraries, versioning, and platform differences.

Managing Libraries and Dependencies

An introduction to traditional build tools like Makefiles and IDE projects, highlighting the maintenance, scalability, and cross-platform challenges that result

Automated Build Systems and Their Limitations

Project Structure and Dependency Management

Setting up CMake and creating your first project

An introduction to CMake, the cross-platform, open-source meta-build system that solves the core challenges of C++ project management.

Why CMake?

A step-by-step guide to installing CMake. We'll explore command-line, GUI, and IDE workflows, and how to configure your compiler.

Setting Up a CMake Environment

Creating the bare minimum `CMakeLists.txt` file and build up to a project with an executable and a library, learning the fundamental commands along the way.

Writing a CMakeLists File

Learn the two-stage process of building a CMake project. This lesson covers configuring, generating, building, running, and troubleshooting common errors.

Building and Running CMake Projects

Learn how to organize large C++ projects in CMake using subdirectories and the `add_subdirectory()` command to create modular, maintainable builds.

CMake Project Structure and Subdirectories

Introducing CMake

Exploring CMake's programming features, caching mechanisms, and interactions with the file system

A primer on the fundamental building blocks of the CMake language: variables, string interpolation, and logging messages.

CMake Variables and Logging

Discover the CMake Cache, the mechanism for storing persistent user-configurable options, and learn how to create and manage cached variables.

The CMake Cache

Exploring CMake's command syntax and its list data type, including how to create, manipulate, and use lists in your projects.

CMake Commands, Arguments, and Lists

Transform your static build descriptions into dynamic scripts with CMake's control flow commands, if() and foreach().

CMake Conditionals and Loops

Exploring CMake's `file()` command, covering how to read, write, copy, and glob files, and how to manipulate paths.

Working with the File System

Learn to create reusable logic with CMake functions and macros, and how to organize them into modules for clean, scalable build systems.

Modularizing CMake Code

CMake Language and Fundamentals

Learn more about targets, and how their properties and links are the foundation of modern CMake

Learn to work with target properties - understanding their purpose, setting and updating them, and retrieving them when your build requires it

Target Properties

Learn about target types, and how the `PUBLIC`, `PRIVATE`, and `INTERFACE` keywords control how properties are shared.

Relationships Between Targets

Discover how CMake's `target_link_libraries()` command builds a dependency graph, and how it uses this graph to automatically manage transitive dependencies and link order.

Indirect Relationships and the Dependency Graph

Learn to use abstract target types like `INTERFACE`, `ALIAS`, and `IMPORTED` to model complex project needs, organize build properties, and integrate pre-compiled binaries.

Using `INTERFACE`, `ALIAS`, and `IMPORTED` Libraries

Adding support for user-configurable library types and an initial introduction to target installation.

Using Shared Libraries

Targets and Dependencies

Learn how to manage multiple build configurations within the same project, and how to create scripts that adapt to the environment

Learn how CMake manages different build configurations like Debug and Release, the difference between generator types, and how to apply settings conditionally using modern techniques.

Build Configurations (Debug, Release, etc.)

Learn the tools and patterns needed to write portable CMakeLists.txt files that work seamlessly across platforms and compilers

Cross-Platform Configurations

Learn to write architecture-aware CMake scripts for 32/64-bit systems and Apple's Universal Binaries (Intel/ARM).

Handling Different Architectures

Learn to build for different operating systems and architectures using CMake's cross-compilation support and toolchain files.

Cross-Compilation and Toolchain Files

Platforms and Build Configurations

Learn how to add external libraries to our build, and how to create our own packages for others to use

Learn to find and use pre-installed libraries with `find_package()`, understanding Module vs. Config mode and the power of imported targets

Finding Dependencies using `find_package()`

Learn how to make your libraries consumable by other projects using CMake by turning your build-tree into a distributable install-tree.

Creating a Consumable Package

Learn how to consume a CMake package you've installed, completing the producer-consumer cycle. We'll cover using `find_package()` and how to point CMake to your library's location

Using Installed Packages

Learn how to create self-contained, reproducible builds by fetching dependencies from source using `FetchContent()` and `ExternalProject_Add()`.

Fetching External Dependencies

Creating and Consuming Packages

Integrating the vcpkg and Conan package managers to handle external dependencies

Learn to manage C++ dependencies effortlessly with the vcpkg package manager and its seamless CMake integration using toolchain files and manifests.

Integrating CMake and vcpkg

Learn the day-to-day commands for managing dependencies with vcpkg and how to guarantee reproducible builds using baselines and version overrides

vcpkg Commands and Versioning

Combine vcpkg with CMake's cross-compilation capabilities. Learn to use triplets to target different architectures and chain-load toolchain files

Cross-Compiling with vcpkg

Discover Conan, the binary-first C++ package manager, and learn how to integrate it with CMake using profiles and lockfiles for fast, reproducible builds.

Using Conan with CMake

Learn to cross-compile C++ projects with Conan, using build and host profiles to manage dependencies for different platforms.

Cross-Compilation with Conan

Package Managers

Introducing presets, allowing us to simplify our workflow and share common configurations across our team

Learn the fundamentals of CMake Presets. This lesson introduces `CMakePresets.json` to simplify complex build commands.

CMake Presets

Learn to structure your CMake presets with inheritance and customize them for your local environment using `CMakeUserPresets.json`.

User Presets and Inheritance

Learn to make your CMake presets portable by using environment variables and conditional logic to adapt to different machine configurations.

Environment Variables and Conditional Presets

Learn how to extend the build process beyond simple compilation by integrating external tools and custom commands

Learn how to use generator expressions `$<...>` for build-time conditional logic.

Generator Expressions and Conditional Logic

Learn how to attach commands to specific points in a target's build lifecycle using `add_custom_command(TARGET)` for tasks like post-build file copying.

Integrating Tools with `add_custom_command()`

Learn to integrate code generators like Protobuf into your build process using `add_custom_command()`, creating automated file-level dependencies in CMake.

Generating Files with Protobuf and `add_custom_command()`

Custom Commands

An introduction to automating common workflow chores by creating custom targets

Learn to create standalone, non-code targets using add_custom_target() for utility tasks like code formatting and documentation generation.

Custom Targets and Build Utilities

Using the Clang-Format tool to automatically enforce a consistent coding style, and integrating it into our build using a custom CMake target

Using Clang-Format

An introduction to file sets and the `target_sources()` command, allowing us to manage our header files in a more controlled way

Using File Sets

A step-by-step guide to integrating Doxygen into your CMake build process for automated C++ documentation.

Generating Documentation with Doxygen

Custom Targets

Integrate automated tests into a CMake project using CTest, GoogleTest, and GoogleMock

Learn the fundamentals of automated testing in C++ with CTest. Create your first test suite, register tests with `add_test()`, and run them with the `ctest` command.

Automated Testing and CTest

Learn to refactor manual C++ tests into a professional test suite using the GoogleTest framework, managed by vcpkg and integrated with CTest.

Integrating GoogleTest with CMake

Learn to use a wide range of assertions, write data-driven parameterized tests, and isolate dependencies with Google Mock.

Assertions and Parameterized Tests

Learn how to isolate dependencies that our project uses, and then how to replace those dependencies at test time using Google Mock.

Dependency Injection and Mocking

Learn the standard pattern for testing application logic by refactoring it into a linkable library

Testing Executables

Move beyond individual tests to manage a full test suite. Learn to organize tests, run tests in parallel, filter by labels, and measure our code coverage

Managing a Test Suite

Automated Testing

Using static and dynamic analysis in CMake projects to automatically detect memory bugs, performance issues, and problematic code

Learn to find memory bugs at runtime by integrating AddressSanitizer (ASan) into a CMake project

Using AddressSanitizer (ASan)

Adding benchmarks to our build to measure the performance of our code, and track how it changes over time

Using Google Benchmark

Integrating Clang-Tidy to enforce good practices and find bugs before we even compile

Using Clang-Tidy

Automated Analysis

Automating the entire development lifecycle using Git for souce control, GitHub Actions for cloud builds, and CPack to create distributable packages and installers.

An introduction to Git, the version control system that underpins modern collaborative software development and automated build pipelines.

Source Control with Git

Learn how to automate your build and test process with Continuous Integration (CI) and a build server. This lesson provides a guide to creating a GitHub Actions workflow for a CMake project.

Build Servers and Continuous Integration

Learn to package your C++ projects using CMake's CPack. This lesson covers creating developer ZIP archives and professional Windows MSI installers with components.

Packaging with CPack

Learn to automate the packaging process by integrating CPack with a multi-platform GitHub Actions workflow

Packaging with GitHub Actions

Learn to automate the creation of a professional Windows installer by integrating CPack, the WiX Toolset, and a multi-platform GitHub Actions workflow.

Creating Installers with CPack and GitHub Actions

Learn to automate the release process using a GitHub Actions workflow that builds, packages, and publishes artifacts to a GitHub Release

Deployment with GitHub Releases

Continuous Integration and Packaging

If you have a basic grasp of C++ but find the build process intimidating, this course is for you. We assume you know the language, but not how to manage large projects.

Manage complex, cross-platform builds using industry-standard tools. Create scalable build systems that automate the entire development lifecycle.

This course is a comprehensive deep-dive into the entire C++ build ecosystem, centered around the power and flexibility of modern CMake. We leave no stone unturned.

Start with a foundational understanding of compilers, linkers, static vs. shared libraries, and object files.

The Full Build Pipeline

Learn every aspect of the CMake language, from variables and control flow to functions, modules, and generator expressions.

Complete CMake Language Mastery

Master the modern target-centric paradigm, including properties, usage requirements, and abstract targets.

Advanced Target Management

Explore every method for handling dependencies, from `find_package` and `FetchContent` to vcpkg and Conan, including versioning and cross-compilation.

In-Depth Dependency Management

Integrate a full suite of professional tools for testing (CTest, GoogleTest), analysis (ASan, Clang-Tidy), and packaging (CPack).

Total Workflow Automation

Build a fully automated deployment pipeline that takes your code from a Git push to a published GitHub Release with multi-platform installers.

End-to-End CI/CD and Deployment

What You'll Learn

Become a software engineer with C++.  Starting from the basics, we guide you step by step along the way

Intro to C++ Programming

If you're completely new to programming or want a thorough introduction, consider starting with our free introductory course:

New to Programming?

Managing C++ Projects Using CMake

In the last lesson, we integrated GoogleTest into our project, replacing our clunky manual test driver. We learned the basics: the `TEST` and `TEST_F` macros, the simple `EXPECT_EQ` assertion, and how to automate test discovery with `gtest_discover_tests()`.


This is a solid foundation, but GoogleTest offers a much richer set of tools. Real-world code has more complex requirements. How do you test for exceptions? How do you verify a function's behavior across dozens of different inputs without writing dozens of repetitive tests?


This lesson will answer those questions as we introduce three more advanced testing features:

- A wider range of **assertion macros** for handling different data types and failure modes.
- **Parameterized tests** for writing data-driven test cases.
- **Custom matchers** for creating expressive and reusable assertions for complex conditions

## Assertion Macros


So far, we've only used `EXPECT_EQ`, but GoogleTest provides a wide range of assertion macros for different situations. The first and most important distinction to learn is between the two "flavors" of every assertion: `EXPECT_*` and `ASSERT_*`.


### Fatal vs. Non-Fatal Failures

- **`EXPECT_*`**: These are **non-fatal** assertions. If an `EXPECT_*` assertion fails, it records the failure and prints an error message, but the current test function **continues to run**. This is what we want most of the time, particularly if the function has multiple assertions, as it allows us to see all the failures, not just the first one.
- **`ASSERT_*`**: These are **fatal** assertions. If an `ASSERT_*` assertion fails, it immediately **aborts the current test function**.  Sometimes, there will be no point in continuing the test if a fundamental assumption is violated, and it may even be unsafe to do so.  In those situations, fatally failing is more appropriate.

Let's evolve our `Greeter` class to see some of these new assertions in action. We'll add an "enthusiasm level" that affects the greeting:


<CodeViewer>


<CodeFile path="greeter/include/greeter/Greeter.h" status="Updated">


```c++
#pragma once
#include <string>

class Greeter {
public:
  void setEnthusiasm(float level); //<h>
  float getEnthusiasm() const;   //<h>
  std::string greet() const;

private:
  float enthusiasm_ = 0.5f; //<h>
};
```


</CodeFile>


<CodeFile path="greeter/src/Greeter.cpp" status="Updated">


```c++
#include <greeter/Greeter.h>
#include <stdexcept>

void Greeter::setEnthusiasm(float level) {
  if (level < 0.0f || level > 1.0f) {
    throw std::out_of_range(
      "Enthusiasm must be between 0.0 and 1.0"
    );
  }
  enthusiasm_ = level;
}

float Greeter::getEnthusiasm() const {
  return enthusiasm_;
}

std::string Greeter::greet() const {
  std::string greeting{"Hello from the Greeter class"};
  int exclamations{static_cast<int>(enthusiasm_ * 3)};
  for (int i = 0; i < exclamations; ++i) {
    greeting += "!";
  }
  return greeting;
}
```


</CodeFile>


</CodeViewer>


Now, let's add some new tests for this feature.


### Floating-Point Comparisons with `EXPECT_NEAR`


Directly comparing floating-point numbers with `==` is a classic programming error due to precision issues. GoogleTest provides `EXPECT_NEAR` and `ASSERT_NEAR` to solve this. They take a third argument: the acceptable tolerance:


<CodeFile src="tests/main.cpp">


```c++
#include <gtest/gtest.h>
#include <greeter/Greeter.h>

class GreeterTest : public testing::Test {
protected:
  Greeter my_greeter;
};

TEST_F(GreeterTest, EnthusiasmIsSetCorrectly) {
  my_greeter.setEnthusiasm(0.7f);
  EXPECT_NEAR(//<h>
    my_greeter.getEnthusiasm(), 0.7f, 0.001//<h>
  );//<h>
}
```


</CodeFile>


### Exception Testing with `ASSERT_THROW`


Our `setEnthusiasm()` method is designed to throw an exception if the input is invalid. Let's add a test to verify this behavior. `EXPECT_THROW` and `ASSERT_THROW` check that a given statement throws an exception of a specific type.


<CodeFile src="tests/main.cpp">


```c++
#include <gtest/gtest.h>
#include <greeter/Greeter.h>

class GreeterTest : public testing::Test {
protected:
  Greeter my_greeter;
};

TEST_F(GreeterTest, EnthusiasmIsSetCorrectly) {
  my_greeter.setEnthusiasm(0.7f);
  EXPECT_NEAR(
    my_greeter.getEnthusiasm(), 0.7f, 0.001
  );
}

TEST_F(//<h>
  GreeterTest,//<h>
  SetEnthusiasmThrowsOnInvalidInput//<h>
) {//<h>
  EXPECT_THROW(//<h>
    my_greeter.setEnthusiasm(1.1f),//<h>
    std::out_of_range//<h>
  );//<h>
}
```


</CodeFile>


### Other Useful Assertion Macros


Beyond the basics, Google Test provides a rich set of macros for various common checks. Here are some of the most frequently used:


**Boolean Checks**:

- `EXPECT_TRUE(condition)` / `ASSERT_TRUE(condition)`: Verifies that a condition is true.
- `EXPECT_FALSE(condition)` / `ASSERT_FALSE(condition)`: Verifies that a condition is false.

**Relational Comparisons**: For numeric types, these are often more readable than using `EXPECT_TRUE` with a comparison operator.

- `EXPECT_NE(val1, val2)`: Not equal (`!=`).
- `EXPECT_LT(val1, val2)`: Less than (`<`).
- `EXPECT_LE(val1, val2)`: Less than or equal to (`<=`).
- `EXPECT_GT(val1, val2)`: Greater than (`>`).
- `EXPECT_GE(val1, val2)`: Greater than or equal to (`>=`).

**String Comparisons**: While `EXPECT_EQ` works perfectly for `std::string`, you need special macros for C-style strings (`char*`).

- `EXPECT_STREQ(str1, str2)`: Verifies two C-style strings have the same content.
- `EXPECT_STRCASEEQ(str1, str2)`: The case-insensitive version of `EXPECT_STREQ`.

**Death Tests**: For testing that code exits prematurely (e.g., via `assert()` or `exit()`).

- `EXPECT_DEATH(statement, regex)`: Verifies that a statement causes the process to terminate with a non-zero exit code and that its standard error output matches a regular expression.

The full list of available assertions are on the [official docs](https://google.github.io/googletest/reference/assertions.html).


<Pw>


## Matchers


Sometimes, a simple equality or relational check isn't enough. You might want to verify that a string has a certain format, that a collection contains a specific element, or that a value falls within a complex range.


We could implement each of these checks manually within our test function, but more commonly, test libraries like Google Test allow us to define **matchers** that integrate with their APIs more elegantly.


Matchers are objects that represent a predicate. In Google Test, they are used with the `EXPECT_THAT(value, matcher)` assertion, which reads like natural language: "I expect that this value matches this condition."


We can create any matcher (predicate function) that we might need, but The **Google Mock** library, which is bundled with Google Test, includes a large collection of generally useful examples.


To use them, we typically need to include the `<gmock/gmock.h>` header and link against the `GTest::gmock` library.


Let's update `tests/CMakeLists.txt` to include it.


<CodeFile path="tests/CMakeLists.txt" status="Updated">


```c++
#<cmake>
cmake_minimum_required(VERSION 3.23)
find_package(GTest REQUIRED)

add_executable(GreeterTests main.cpp)

target_link_libraries(GreeterTests PRIVATE
  GreeterLib
  GTest::gtest
  GTest::gmock # Add the gmock library #<h>
  GTest::gtest_main
)

gtest_discover_tests(GreeterTests)
```


</CodeFile>


Here are a few of the many built-in matchers that Google Mock provides:

- `testing::Eq(value)`: Equivalent to `==`.
- `testing::Gt(value)`: Greater than (`>`).
- `testing::StartsWith(str)`: Checks if a string starts with a given prefix.
- `testing::EndsWith(str)`: Checks if a string ends with a given suffix.
- `testing::ContainsRegex(regex)`: Checks if a string contains a substring that matches a regular expression.
- `testing::Each(matcher)`: Checks if every element in a container matches an inner matcher.
- `testing::AllOf(m1, m2, ...)`: A logical AND; succeeds if all nested matchers succeed.

The [official docs](https://google.github.io/googletest/reference/matchers.html) contain the full list of available matchers.


### Practical Example


In our parameterized test, we checked for the exact number of exclamation marks. This is a bit brittle; what if we only care that the greeting has the correct base string and ends with _some_ number of exclamation marks? A matcher is perfect for this.


Let's add a test that uses a **regular expression matcher** to verify the format:


<CodeFile path="tests/main.cpp" status="Updated">


```c++
#include <gtest/gtest.h>
#include <gmock/gmock.h> // Include for matchers //<h>
#include <greeter/Greeter.h>
#include <tuple>

class GreeterTest : public testing::Test {
protected:
  Greeter my_greeter;
};

TEST_F(GreeterTest, GreetHasCorrectFormat) {//<h>
  my_greeter.setEnthusiasm(0.8f);
  EXPECT_THAT(
    my_greeter.greet(),
    testing::MatchesRegex("Hello from the Greeter class!*")//<h>
  );
}
```


</CodeFile>


The `*` character is a special regex token that matches any quantity (including 0) of the proceeding token.  We have <InlineLessonPromo url="pro-cpp/regex" title="Regular Expressions" description="An introduction to regular expressions, and how to use them in C++ with `std::regex`, `std::regex_match`, and `std::regex_search`">dedicated lessons</InlineLessonPromo> on regular expressions in our C++ course.


In this example, our test is ensuring that the function returns "Hello from the Greeter class" followed by any number of exclamation marks.


```c++
//<ti>
cmake --build --preset default
```


```c++
//<ti>
ctest --preset default
```


```c++
//<o>
Test project D:/Projects/cmake/build
    Start 1: GreeterTest.GreetHasCorrectFormat
1/1 Test #1: GreeterTest.GreetHasCorrectFormat ...   Passed    0.01 sec

100% tests passed, 0 tests failed out of 1

Total Test time (real) =   0.04 sec
```


This test is more robust to small, insignificant changes in the implementation of `greet()`.


## Parameterized Tests


Let's go back to our previous example where we were testing the exact output of `greet()`, not just testing the general format.


The exact behavior that depends an externally-provided parameter - the enthusiasm level. What if we want to test the `greet()` method for several different enthusiasm levels? We could write a separate test for each one:


```c++
TEST_F(GreeterTest, GreetsWithNoExclamations) {
  my_greeter.setEnthusiasm(0.1f);
  EXPECT_EQ(
    my_greeter.greet(),
    "Hello from the Greeter class"
  );
}

TEST_F(GreeterTest, GreetsWithOneExclamation) {
  my_greeter.setEnthusiasm(0.4f);
  EXPECT_EQ(
    my_greeter.greet(),
    "Hello from the Greeter class!"
  );
}
// ... and so on ...
```


This is repetitive and doesn't scale up to real-world scenarios where we might want to test dozens or hundreds of different parameter combinations. The solution is a **parameterized test**. This allows you to run the same test logic with a wide range of different input values.


Let's refactor our enthusiasm tests into a single parameterized test.  The setup involves four steps.


### Step 1: Use `TestWithParam`


First, we'll update our a test fixture class to inherit from `testing::TestWithParam<T>`, instead of `testing::Test`. 


The template parameter `T` in `TestWithParam` is the type of our parameter.  We often use a `std::tuple` for this, as our parameter typically contain multiple values of different types.


In our case, our tuple will contain a `float` representing the `enthusiasm_` level, and a `std::string` representing what we expect our function to return given that `enthusiasm_` level:


<CodeFile path="tests/main.cpp" status="Updated">


```c++
#include <gtest/gtest.h>
#include <greeter/Greeter.h>
#include <tuple>

class GreeterEnthusiasmTest :
  public testing::TestWithParam<//<h>
    std::tuple<float, std::string>//<h>
  >//<h>
{
protected:
  Greeter my_greeter;
};

// ...
```


</CodeFile>


### Step 2: Writing a Parameterized Test with `TEST_P`


Next, we use the `TEST_P` macro (the 'P' is for Parameterized) instead of `TEST_F`. 


Inside the test, we can access the current parameter value with the `GetParam()` method.


We'll pull our enthusiasm and expected greeting from our tuple.  We'll then set our enthusiasm to that level, and assert that the `greet()` method then returns the expected string:


<CodeFile path="tests/main.cpp" status="Updated">


```c++
#include <gtest/gtest.h>
#include <greeter/Greeter.h>
#include <tuple>

class GreeterEnthusiasmTest :
  public testing::TestWithParam<
    std::tuple<float, std::string>
  >
{
protected:
  Greeter my_greeter;
};

TEST_P(//<h>
  GreeterEnthusiasmTest,//<h>
  GreetReturnsCorrectExclamations//<h>
) {//<h>
  auto [enthusiasm, expected_greeting]{GetParam()};//<h>
  my_greeter.setEnthusiasm(enthusiasm);//<h>
  EXPECT_EQ(my_greeter.greet(), expected_greeting);//<h>
}//<h>

// ...
```


</CodeFile>


### Step 3: Instantiating Tests


Finally, we just need to instantiate our tests with different parameter values.


We use the `INSTANTIATE_TEST_SUITE_P` macro for this, passing a name as the first argument, our `TEST_P`'s name as the second argument, and the set of parameters we want to test as the third.


The `testing::Values()` function helps us to provide these parameters.  This allowing us to now instantiate each test simply by constructing a parameter - often a single line of code:


<CodeFile path="tests/main.cpp" status="Updated">


```c++
#include <gtest/gtest.h>
#include <greeter/Greeter.h>
#include <tuple>

class GreeterEnthusiasmTest :
  public testing::TestWithParam<
    std::tuple<float, std::string>
  >
{
protected:
  Greeter my_greeter;
};

TEST_P(
  GreeterEnthusiasmTest,
  GreetReturnsCorrectExclamations
) {
  auto [enthusiasm, expected_greeting]{GetParam()};
  my_greeter.setEnthusiasm(enthusiasm);
  EXPECT_EQ(my_greeter.greet(), expected_greeting);
}

INSTANTIATE_TEST_SUITE_P(//<h>
  EnthusiasmLevels,//<h>
  GreeterEnthusiasmTest,//<h>
  testing::Values(//<h>
    std::make_tuple(0.0f, "Hello from the Greeter class"),//<h>
    std::make_tuple(0.3f, "Hello from the Greeter class"),//<h>
    std::make_tuple(0.4f, "Hello from the Greeter class!"),//<h>
    std::make_tuple(0.7f, "Hello from the Greeter class!!"),//<h>
    std::make_tuple(1.0f, "Hello from the Greeter class!!!")//<h>
  )//<h>
);//<h>
```


</CodeFile>


### Step 4: Running the Tests


When we run our tests now, CTest and Google Test will automatically generate and run five separate test cases based on this one definition, each with a different set of inputs from the `Values` list.


```c++
//<ti>
cmake --build --preset default
```


```c++
//<ti>
ctest --preset default
```


```c++
//<o>
...
100% tests passed, 0 tests failed out of 5
...
```


## Summary


This lesson introduced most of the core features we need to write tests.

- **Assertions**: We learned the difference between fatal `ASSERT_*` and non-fatal `EXPECT_*` assertions, and saw specialized assertions for floating-point numbers (`EXPECT_NEAR`) and exceptions (`ASSERT_THROW`).
- **Parameterized Tests**: We saw how to use `TEST_P` and `INSTANTIATE_TEST_SUITE_P` to eliminate repetitive code by running the same test logic against a large set of input data.
- **Custom Matchers**: We learned to use `EXPECT_THAT` with Google Mock's library of matchers to create expressive assertions for complex conditions, such as matching a regular expression.

<Preview>


### Dependency Injection and Mocking


Our tests have so far focused on a `Greeter` class that is entirely self-contained. But what happens when our code has dependencies on other complex systems, like a database, a network connection, or even just the system clock? Running tests against these real dependencies can make them slow, unreliable, and hard to set up.


In the next lesson, we will introduce **dependency injection** as a design pattern and **mocking** with Google Mock as the testing technique to solve this problem. We'll learn how to create "fake" versions of our dependencies to isolate the code under test, leading to fast, deterministic, and reliable test runs.


</Preview>


</Pw>

Google Test Integration with vcpkg

Advanced Unit Testing Topics

Advanced Guide

Assertions Reference

For Dummies

Cookbook

Back to Basics: Testing in C++ - Phil Nash

Mocks Aren't Stubs

Master advanced C++ unit testing with this Google Test tutorial. Learn the difference between ASSERT and EXPECT, how to create custom matchers, and how to use parameterized tests for data-driven validation.

Advanced Google Test Tutorial: Assertions, Parameterized Tests & Mocking

Floating-Point Comparisons with `EXPECT_NEAR`

Step 2: Writing a Parameterized Test with `TEST_P`

Assertions and Parameterized Tests

Understanding the Basics

Practical Applications

Common Mistakes

Next Steps

Additional Resources

Assessment Preparation

Managing C++ Projects Using CMake