PEST is a modern testing framework for PHP that focuses on clean syntax, readability, and developer experience. It builds on PHPUnit but provides a much more expressive and minimalistic interface.

📌 PEST = "PHP Testing for Humans"
It’s designed for developers who want to write fast, readable, and elegant tests — with less boilerplate.

🚀 Why Use PEST Instead of PHPUnit?

PEST is built on top of PHPUnit, but it:

• Provides a cleaner, simpler syntax

• Removes unnecessary structure

• Encourages a functional, behavior-driven style

• Still supports traditional PHPUnit classes if needed

🔍 Example – PHPUnit vs. PEST

PHPUnit:

class UserTest extends TestCase
{
    public function test_user_has_name()
    {
        $user = new User('John');
        $this->assertEquals('John', $user->name);
    }
}

PEST:

it('has a name', function () {
    $user = new User('John');
    expect($user->name)->toBe('John');
});

👉 Much shorter and easier to read — especially when writing many tests.

🧩 Key Features of PEST

• ✅ Elegant, expressive syntax (inspired by Jest/Mocha)

• 🧪 Supports unit, feature, API, and browser-based testing

• 🧱 Data-driven testing via with([...])

• 🧬 Test hooks like beforeEach() / afterEach()

• 🎨 Fully extensible with plugins and custom expectations

• 🔄 Fully compatible with PHPUnit — you can run both side by side

🛠️ Installation

In a Laravel or Composer project:

composer require pestphp/pest --dev
php artisan pest:install  # for Laravel projects

Then run tests:

./vendor/bin/pest

🧠 Summary

PEST is ideal if you:

• Want to write tests that are fun and easy to maintain

• Prefer clean, modern syntax

• Already use PHPUnit but want a better experience

💡 Many Laravel developers are adopting PEST because it integrates seamlessly with Laravel and truly makes testing feel "human" — just like its slogan says.

🛠️ What is GitHub Actions?

GitHub Actions is a feature of GitHub that lets you create automated workflows for your software projects—right inside your GitHub repository.

📌 What can you do with GitHub Actions?

You can build CI/CD pipelines (Continuous Integration / Continuous Deployment), such as:

• ✅ Automatically test code (e.g. with PHPUnit, Jest, Pytest)

• 🛠️ Build your app on every push or pull request

• 🚀 Automatically deploy (e.g. to a server, cloud platform, or DockerHub)

• 📦 Create releases (e.g. zip packages or version tags)

• 🔄 Run scheduled tasks (cronjobs)

🧱 How does it work?

GitHub Actions uses workflows, defined in a YAML file inside your repository:

• Typically stored as .github/workflows/ci.yml

• You define events (like push, pull_request) and jobs (like build, test)

• Each job consists of steps, which are shell commands or prebuilt actions

Example: Simple CI Workflow for Node.js

name: CI

on: [push]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: '20'
      - run: npm install
      - run: npm test

🧩 What are "Actions"?

An Action is a single reusable step in a workflow. You can use:

• Prebuilt actions (e.g. actions/checkout, setup-node, upload-artifact)

• Custom actions (e.g. shell scripts or Docker-based logic)

You can explore reusable actions in the GitHub Marketplace.

💡 Why use GitHub Actions?

• Saves time by automating repetitive tasks

• Improves code quality through automated testing

• Enables consistent, repeatable deployments

• Integrated directly in GitHub—no need for external CI tools like Jenkins or Travis CI

The "Happy Path" (also known as the "Happy Flow") refers to the ideal scenario in software development or testing where everything works as expected, no errors occur, and all inputs are valid.

Example:

Let’s say you’re developing a user registration form. The Happy Path would look like this:

1. The user enters all required information correctly (e.g., a valid email and secure password).

2. They click “Register.”

3. The system successfully creates an account.

4. The user is redirected to a welcome page.

➡️ No validation errors, no server issues, and no unexpected behavior.

What is the purpose of the Happy Path?

• Initial testing focus: Developers and testers often check the Happy Path first to make sure the core functionality works.

• Basis for use cases: In documentation or requirements, the Happy Path is typically the main scenario before covering edge cases.

• Contrasts with edge cases / error paths: Anything that deviates from the Happy Path (e.g., missing password, server error) is considered an "unhappy path" or "alternate flow."

A Partial Mock is a testing technique where only certain methods of an object are mocked, while the rest of the object retains its real implementation. This is useful when you want to stub or mock specific methods but keep others functioning normally.

When to Use a Partial Mock?

• When you want to test a class but isolate certain methods.

• When some methods are difficult to test (e.g., they have external dependencies), but others should retain their real logic.

• When you only need to stub specific methods to control test behavior.

Example in PHP with PHPUnit

Suppose you have a Calculator class but want to mock only the multiply() method while keeping add() as is.

class Calculator {
    public function add($a, $b) {
        return $a + $b;
    }

    public function multiply($a, $b) {
        return $a * $b;
    }
}

// PHPUnit Test with Partial Mock
class CalculatorTest extends \PHPUnit\Framework\TestCase {
    public function testPartialMock() {
        // Create a Partial Mock for Calculator
        $calculator = $this->getMockBuilder(Calculator::class)
                           ->onlyMethods(['multiply']) // Only mock this method
                           ->getMock();

        // Define behavior for multiply()
        $calculator->method('multiply')->willReturn(10);

        // Test real add() method
        $this->assertEquals(5, $calculator->add(2, 3));

        // Test mocked multiply() method
        $this->assertEquals(10, $calculator->multiply(2, 3));
    }
}

Here, add() remains unchanged and executes the real implementation, while multiply() always returns 10.

Conclusion

Partial Mocks are useful when you need to isolate specific parts of a class without fully replacing it. They help make tests more stable and efficient by mocking only selected methods.

A SUT (System Under Test) is the system or component being tested in a testing process. The term is commonly used in software development and quality assurance.

Meaning and Application:

• In software testing, the SUT refers to the entire program, a single module, or a specific function being tested.
• In hardware testing, the SUT could be an electronic device or a machine under examination.
• In automated testing, the SUT is often tested using frameworks and tools to identify errors or unexpected behavior.

A typical testing process includes:

1. Defining test cases based on requirements.
2. Executing tests on the SUT.
3. Reviewing test results and comparing them with expected outcomes.

Jest is a JavaScript testing framework developed by Meta (Facebook). It is mainly used for testing JavaScript and TypeScript applications, especially React applications, but it also works well for Node.js backends.

Key Features of Jest:

• Easy Configuration: Jest works "out of the box" with minimal setup.
• Speed: It uses parallelization and intelligent caching for fast test execution.
• Snapshot Testing: Ideal for UI tests to ensure the output remains consistent.
• Mocking & Spying: Allows replacing dependencies with mock functions.
• Code Coverage Reports: Shows how much of the code is covered by tests.

Example of a Simple Jest Test:

// sum.js
function sum(a, b) {
  return a + b;
}
module.exports = sum;

// sum.test.js
const sum = require('./sum');

test('addiert 1 + 2 und ergibt 3', () => {
  expect(sum(1, 2)).toBe(3);
});

o run the test, use:

jest

Or, if installed locally in a project:

npx jest

The Pyramid Web Framework is a lightweight, flexible, and scalable web framework for Python. It is part of the Pylons Project family and is ideal for developers looking for a minimalist yet powerful solution for web applications.

Key Features of Pyramid:

1. Minimalistic but Extensible

   • Pyramid provides a lean core architecture with only essential features, allowing developers to add extensions as needed.

2. Flexible

   • It supports various databases, authentication systems, and templating engines (e.g., Jinja2, Chameleon, Mako).

3. Traversal and URL Mapping

   • Pyramid allows both traditional URL routing (similar to Flask/Django) and a powerful traversal system, which is particularly useful for hierarchical data structures.

4. Powerful and Efficient

   • Due to its modular design, Pyramid is suitable for large projects while remaining resource-efficient.

5. First-Class Testing Support

   • Pyramid is built with testability in mind and includes built-in support for unit and integration testing.

6. Comprehensive Documentation & Community Support

   • The official documentation is extensive, and there is an active developer community.

When Should You Use Pyramid?

• If you need a lightweight yet scalable framework.
• If you want full control over your application architecture.
• If you’re developing a project with complex URL structures or hierarchical data.
• If Django feels too heavy and Flask feels too basic.

Comparison with Other Frameworks:

Conclusion

Pyramid is an excellent choice for developers looking for a balance between minimalism and power. It is particularly well-suited for medium to large web projects where scalability, flexibility, and good testability are essential.

A/B testing is a method used in marketing, web design, and software development to compare two or more versions of an element to determine which one performs better.

How does A/B testing work?

1. Splitting the audience: The audience is divided into two (or more) groups. One group (Group A) sees the original version (control), while the other group (Group B) sees an alternative version (variation).

2. Testing changes: Only one specific variable is changed, such as a button color, headline, price, or layout.

3. Measuring results: User behavior is analyzed, such as click rates, conversion rates, or time spent. The goal is to identify which version yields better results.

4. Data analysis: Results are statistically evaluated to ensure that the differences are significant and not due to chance.

Examples of A/B testing:

• Websites: Testing two different landing pages to see which one generates more leads.
• Emails: Comparing subject lines to determine which leads to higher open rates.
• Apps: Testing changes in the user interface (UI) to improve usability.

Benefits:

• Provides data-driven decision-making.
• Reduces risks when making design or functionality changes.
• Improves conversion rates and efficiency.

Drawbacks:

• Can be time-consuming if data collection is slow.
• Results may not always be clear, especially with small sample sizes.
• External factors can impact the test.

Cyclomatic complexity is a metric used to assess the complexity of a program's code or software module. It measures the number of independent execution paths within a program, based on its control flow structure. Developed by Thomas J. McCabe, this metric helps evaluate a program’s testability, maintainability, and susceptibility to errors.

Calculating Cyclomatic Complexity

Cyclomatic complexity $V(G)$ is calculated using the control flow graph of a program. This graph consists of nodes (representing statements or blocks) and edges (representing control flow paths between blocks). The formula is:

$$V(G)=E-N+2P$$

• $E$: The number of edges in the graph.
• $N$: The number of nodes in the graph.
• $P$: The number of connected components (for a connected graph, $P=1$).

In practice, a simplified calculation is often used by counting the number of branching points (such as If, While, or For loops).

Interpreting Cyclomatic Complexity

Cyclomatic complexity indicates the minimum number of test cases needed to cover each path in a program once. A higher cyclomatic complexity suggests a more complex and potentially error-prone codebase.

Typical Ranges and Their Meaning:

• 1-10: Low complexity, easy to test and maintain.
• 11-20: Moderate complexity, code becomes harder to understand and test.
• 21-50: High complexity, code is difficult to test and error-prone.
• 50+: Very high complexity, indicating a strong need for refactoring.

Benefits of Cyclomatic Complexity

By measuring cyclomatic complexity, developers can identify potential maintenance issues early and target specific parts of the code for simplification and refactoring.

CaptainHook is a PHP-based Git hook manager that helps developers automate tasks related to Git repositories. It allows you to easily configure and manage Git hooks, which are scripts that run automatically at certain points during the Git workflow (e.g., before committing or pushing code). This is particularly useful for enforcing coding standards, running tests, validating commit messages, or preventing bad code from being committed.

CaptainHook can be integrated into projects via Composer, and it offers flexibility for customizing hooks and plugins, making it easy to enforce project-specific rules. It supports multiple PHP versions, with the latest requiring PHP 8.0​.