Unit Testing

Instructions for AI coding agents on automating unit test creation using consistent software testing patterns in this C++ project.

• [1. Benefits](#1-benefits)
• [2. Principles](#2-principles)

- [2.1. FIRST](#21-first)

• [3. Patterns](#3-patterns)

- [3.1. In-Got-Want](#31-in-got-want) - [3.2. Table-Driven Testing](#32-table-driven-testing) - [3.3. Data-Driven Testing (DDT)](#33-data-driven-testing-ddt) - [3.4. Arrange, Act, Assert (AAA)](#34-arrange-act-assert-aaa) - [3.5. Test Fixtures](#35-test-fixtures) - [3.6. Test Doubles](#36-test-doubles)

• [4. Workflow](#4-workflow)
• [5. Commands](#5-commands)
• [6. Style Guide](#6-style-guide)
• [7. Template](#7-template)

- [7.1. File Header Template](#71-file-header-template) - [7.2. Table-Driven Test Template](#72-table-driven-test-template) - [7.3. Test Fixture Template](#73-test-fixture-template) - [7.4. Exception Test Template](#74-exception-test-template) - [7.5. Boundary Value Test Template](#75-boundary-value-test-template) - [7.6. Data-Driven Test Template (JSON)](#76-data-driven-test-template-json)

• [8. References](#8-references)

1. Benefits

• Readability

> Ensures high code quality and reliability. Tests are self-documenting, reducing cognitive load for reviewers and maintainers.

• Consistency

> Uniform structure across tests ensures predictable, familiar code that team members can navigate efficiently.

• Scalability

> Table-driven and data-driven approaches minimize boilerplate code when adding new test cases, making it simple to expand coverage.

• Debuggability

> Scoped traces and detailed assertion messages pinpoint failures quickly during continuous integration and local testing.

2. Principles

2.1. FIRST

The FIRST principles for unit testing focus on creating effective and maintainable tests.

• Fast

> Unit tests should execute quickly to provide rapid feedback during development and continuous integration.

• Independent

> Each unit test should be self-contained and not rely on the state or behavior of other tests.

• Repeatable

> Unit tests should produce deterministic results every time they are run, regardless of the environment or order of execution.

• Self-Validating

> Unit tests should have clear pass/fail outcomes without requiring manual inspection.

• Timely

> Unit tests should be written and executed early in the development process to catch issues as soon as possible.

3. Patterns

3.1. In-Got-Want

The In-Got-Want pattern structures each test case into three clear sections.

• In

> Defines the input parameters or conditions for the test.

• Got

> Captures the actual output or result produced by the code under test.

• Want

> Specifies the expected output or result that the test is verifying against.

3.2. Table-Driven Testing

Table-driven testing organizes test cases in a tabular format, allowing multiple scenarios to be defined concisely.

• Test Case Structure

> Each row in the table represents a distinct test case with its own set of inputs and expected outputs.

• Iteration

> The test framework iterates over each row, executing the same test logic with different data.

3.3. Data-Driven Testing (DDT)

Data-driven testing separates test data from test logic, enabling the same test logic to be executed with multiple sets of input data.

• External Data Sources

> Test data can be stored in external files (e.g., JSON, CSV) and loaded at runtime.

• Reusability

> The same test logic can be reused with different datasets, enhancing maintainability and coverage.

3.4. Arrange, Act, Assert (AAA)

The AAA pattern structures each test case into three clear phases.

• Arrange

> Set up the necessary preconditions and inputs for the test.

• Act

> Execute the function or method being tested.

• Assert

> Verify that the actual output matches the expected output.

3.5. Test Fixtures

Test fixtures provide a consistent and reusable setup and teardown mechanism for test cases.

• Setup

> Initialize common objects or state needed for multiple tests.

• Teardown

> Clean up resources or reset state after each test.

3.6. Test Doubles

Test doubles (e.g., mocks, stubs, fakes) are simplified versions of complex objects or components used to isolate the unit under test.

• Mocks

> Simulate the behavior of real objects and verify interactions.

• Stubs

> Provide predefined responses to method calls without implementing full behavior.

• Fakes

> Implement simplified versions of real objects with limited functionality.

4. Workflow

1. Identify

Identify new functions in src/ (e.g., src/<module>/<header>.hpp).

1. Add/Create

Create new tests colocated with source code in src/<module>/ (e.g., src/<module>/<header>_test.cpp).

1. Register with CMake

Add the test file to src/<module>/CMakeLists.txt using metagtest() with appropriate options (e.g., WITHDDT).

The test configuration should use ENABLE option with METABUILDTESTING variable:

```cmake include(meta_gtest)

metagtest( ENABLE ${METABUILDTESTING} TARGET ${PROJECTNAME}-test SOURCES <header>test.cpp LINK ${PROJECTNAME}::<module> ) ```

1. Test Coverage Requirements

Include comprehensive edge cases:

- Coverage-guided cases - Boundary values (min/max limits, edge thresholds) - Empty/null inputs - Null pointers and invalid references - Overflow/underflow scenarios - Special cases (negative numbers, zero, special states)

1. Apply Templates

Structure all tests using the template pattern below.

5. Commands

6. Style Guide

• Test Framework

> Use GoogleTest (GTest) framework via #include <gtest/gtest.h>.

• Include Headers

> Include necessary standard library headers (<vector>, <string>, <climits>, etc.) and module-specific headers in a logical order: system headers first, then project headers.

Include necessary headers in this order: 1. GTest/GMock headers (<gtest/gtest.h>, <gmock/gmock.h>) 2. Standard library headers (<memory>, <string>, etc.) 3. Project interface headers 4. Project implementation headers

• Namespace

> Use using namespace <namespace>; for convenience within test functions to reduce verbosity while maintaining clarity, since test scope is limited.

• Test Organization

> Consolidate test cases for a single function into one TEST(...) function using table-driven testing.

This approach: - Eliminates redundant test function definitions - Simplifies maintenance by grouping related scenarios together - Reduces code duplication in setup and teardown phases - Makes it easier to add or modify test cases

• Testing Macros

> Focus each TEST(...) function on a single function or cohesive behavior. For complex setups, use TEST_F fixtures or helper functions to reduce duplication.

• Mocking

> Use Google Mock (GMock) for creating test doubles (mocks, stubs, fakes) to isolate the unit under test. See the [cpp-mock-testing](../cpp-mock-testing/SKILL.md) skill.

• Traceability

> Employ SCOPEDTRACE(tc.label) for traceable failures in table-driven tests.

#include <gtest/gtest.h>

#include <string>
#include <vector>

#include "<module>/<header>.hpp"

using namespace <namespace>;

TEST(<Module>Test, <FunctionName>)
{
  // In-Got-Want
  struct Tests
  {
    std::string label;

    struct In
    {
      /* input types and names */
    } in;

    struct Want
    {
      /* expected output type(s) and name(s) */
    } want;
  };

  // Table-Driven Testing
  const std::vector<Tests> tests = {
    {"case-description-1", {/* input */}, {/* expected */}},
    {"case-description-2", {/* input */}, {/* expected */}},
  };

  for (const auto &tc : tests)
  {
    SCOPED_TRACE(tc.label);

    // Arrange
    <Module> <object>;

    // Act
    auto got = <object>.<function>(tc.in.<input>);

    // Assert
    EXPECT_EQ(got, tc.want.<expected>);
  }
}

class <Module>Test : public ::testing::Test
{
protected:
  void SetUp() override
  {
    // Initialize common objects or state
  }

  void TearDown() override
  {
    // Clean up resources or reset state
  }

  <Module> object_;
};

TEST_F(<Module>Test, <FunctionName>)
{
  // Arrange
  auto input = <input_value>;

  // Act
  auto got = object_.<function>(input);

  // Assert
  EXPECT_EQ(got, <expected>);
}

TEST(<Module>Test, <FunctionName>ThrowsOnInvalidInput)
{
  // Arrange
  <Module> object;
  auto invalid_input = <invalid_value>;

  // Act & Assert
  EXPECT_THROW(object.<function>(invalid_input), <ExceptionType>);
}

TEST(<Module>Test, <FunctionName>BoundaryValues)
{
  // In-Got-Want
  struct Tests
  {
    std::string label;

    struct In
    {
      <input_type> input;
    } in;

    struct Want
    {
      <output_type> expected;
    } want;
  };

  // Table-Driven Testing with boundary cases
  const std::vector<Tests> tests = {
    {"minimum-value", {<MIN_VALUE>}, {/* expected */}},
    {"maximum-value", {<MAX_VALUE>}, {/* expected */}},
    {"zero-value", {0}, {/* expected */}},
    {"empty-input", {{}}, {/* expected */}},
    {"negative-value", {-1}, {/* expected */}},
  };

  for (const auto &tc : tests)
  {
    SCOPED_TRACE(tc.label);

    // Arrange
    <Module> object;

    // Act
    auto got = object.<function>(tc.in.input);

    // Assert
    EXPECT_EQ(got, tc.want.expected);
  }
}

#include <nlohmann/json.hpp>

#include <fstream>

TEST(<Module>Test, <FunctionName>DataDriven)
{
  // Load test data from JSON file
  std::ifstream file("<module>/<header>_test.json");
  nlohmann::json test_data;
  file >> test_data;

  for (const auto &tc : test_data["tests"])
  {
    SCOPED_TRACE(tc["label"].get<std::string>());

    // Arrange
    <Module> object;
    auto input = tc["in"]["input"].get<<input_type>>();
    auto expected = tc["want"]["expected"].get<<output_type>>();

    // Act
    auto got = object.<function>(input);

    // Assert
    EXPECT_EQ(got, expected);
  }
}