API Testing using Python, Behave, VS Code & GitHub Copilot

Lesson 1.0 – API Testing & Development Tools Introduction builds the foundation every modern test engineer and developer needs to stay competitive in today’s fast-paced tech world. The core essentials are broken down step by step — from understanding different API types, request/response formats, HTTP methods, and status codes, to setting up the environment with VS Code and GitHub Copilot for smarter, faster coding. Hands-on practice is included, followed by an API 101 quiz to lock in knowledge. Whether developer, QA, or automation engineer, this lesson delivers the must-have skills to level up testing expertise and hit the ground running.

Lesson 1 – Module Guide provides everything needed to hit the ground running. Inside are quick links to essential tools, setup instructions, and sample code for exploring API types, requests and responses, HTTP methods, and status codes. The guide is designed to keep progress on track throughout Lesson 1.0, reducing time spent searching for resources and maximizing time spent building real-world API testing skills with VS Code and GitHub Copilot.

Lesson 1.1 – Types of APIs highlights the many shapes and sizes APIs take, and why understanding their differences is a game-changer for testers and developers. From the simplicity of REST and the strict XML messaging of SOAP to the flexibility of GraphQL, the speed of gRPC, and the real-time power of WebSockets, each style brings unique strengths. Adding RPC and OAuth rounds out the complete toolkit needed to work with everything from legacy systems to modern, high-performance applications. Mastering these API types provides the skills to confidently test, build, and scale solutions across any environment.

Lesson 1.2 – API Request & Response Formats explains how APIs exchange data — the backbone of every integration. The lesson breaks down the most common formats developers and testers encounter, from JSON and XML to form data, plain text, and high-performance binary protocols like Protocol Buffers. Each format is examined for its impact on readability, performance, and testing strategy, with practical examples to highlight the differences. By the end, handling any request or response format becomes straightforward, ensuring tests remain accurate and adaptable across real-world APIs.

Lesson 1.3 – HTTP Methods & Verbs explores how APIs define actions through the core HTTP methods that power the web. From everyday requests like GET, POST, PUT, and DELETE to specialized verbs such as PATCH, OPTIONS, HEAD, CONNECT, and TRACE, each shapes communication between clients and servers. The lesson breaks down when to use each method, how they impact API behavior, and why understanding them is essential for designing and testing effective workflows. By the end, working with any HTTP method becomes second nature, ensuring tests cover the full range of API interactions.

Lesson 1.4 – HTTP Status Codes breaks down the universal language APIs use to communicate outcomes. From informational responses (100s) and successful calls (200s) to redirects (300s), client errors (400s), server errors (500s), and even custom codes, each provides insight into what happened behind the scenes. The lesson explores what these codes mean, how to interpret them quickly, and why they are critical for debugging and validation in API testing. Mastering status codes provides the knowledge needed to troubleshoot faster and ensure tests are both accurate and reliable.

Lesson 1.5 – Visual Studio (VS) Code focuses on setting up one of the most powerful yet lightweight editors used by developers and testers worldwide. The lesson walks through installation, key features, and navigation techniques designed to improve speed and efficiency. Extensions from the marketplace, built-in Git integration, and the terminal are also explored — bringing everything together to streamline workflows in one place. By the end, VS Code is fully customized and ready to power API testing projects with confidence and efficiency.

Lesson 1.6 – GitHub Copilot unlocks the power of AI-assisted coding to accelerate any workflow. The lesson covers installation, key features, and how Copilot provides real-time code suggestions that make development cleaner, faster, and smarter. Supported languages and frameworks are explored, along with best practices for using AI responsibly and understanding its limitations — knowing when to rely on the tool and when to take the lead. By the end, GitHub Copilot is positioned as an integrated AI pair programmer, boosting productivity and creativity across every project.

Lesson 2.0 – Behavior-Driven Development (BDD) Using Python/Behave introduces one of the most powerful approaches for bridging the gap between business requirements and automated testing. The principles of BDD are broken down step by step, followed by an exploration of the Behave framework and an introduction to Gherkin — the natural language syntax that makes tests easy to read and collaborate on. The lesson covers writing and executing tests with real data, validating results through reporting, and concludes with a Python/Behave 101 quiz to reinforce learning. By the end, participants are equipped to apply BDD in real-world projects, making tests more collaborative, maintainable, and impactful.

Lesson 2 – Module Guide provides a structured roadmap for getting the most out of BDD with Python/Behave. It brings together everything from understanding core BDD concepts and practicing with Gherkin syntax to running Behave tests, validating results, and reviewing progress with the quiz. Designed as a companion playbook, it’s packed with examples, key takeaways, and resources that keep learning on track. Whether used for review after a lecture or during hands-on project work, the guide ensures focus, confidence, and readiness to apply BDD in real-world scenarios.

Lesson 2.1 – Behavior-Driven Development (BDD) introduces the core concepts and mindset behind one of the most effective approaches for aligning business requirements with automated testing. This lesson focuses on understanding how BDD uses shared language and examples to improve collaboration between testers, developers, and stakeholders. Key topics include the purpose of BDD, the Given/When/Then workflow, and how Gherkin scenarios translate business behavior into executable tests. By the end, BDD is positioned as a practical foundation for building clear, maintainable, and business-focused API test automation.

Lesson 2.2 – Behave Framework introduces Python’s leading BDD tool for writing tests in plain English. The framework leverages Gherkin syntax to make scenarios readable and collaborative while keeping project structure simple and scalable. Core topics include organizing scenarios with tags and hooks, enabling data-driven testing, and producing clear reports for validation and communication. By the end, Behave stands out as a practical, business-friendly framework for building maintainable API test suites that bridge the gap between requirements and automation.

Lesson 2.3 – Gherkin Language introduces the simple, structured syntax used to describe software behaviors in human-readable sentences. The lesson covers how features and scenarios are written, how parameterized scenarios and data tables extend flexibility, and how background steps provide context across tests. Multilingual support and behavior-focused design make Gherkin a powerful bridge between technical and non-technical stakeholders. By the end, Gherkin is established as the foundation for writing clear, collaborative, and maintainable BDD test cases.

Lesson 2.4 – Test Data & Execution focuses on how Python Behave enables structured test data management and reliable execution. Key topics include writing step definitions, working with parameterized steps and data tables, and applying background keywords for shared context. The lesson also covers hooks for setup and teardown, managing fixtures and context objects, and running tests efficiently. By the end, Behave is shown as a complete solution for organizing data-driven scenarios and executing them with clarity and precision in real-world API testing.

Lesson 2.5 – Validation & Reporting focuses on validating test outcomes and generating clear, actionable reports. The lesson covers using basic assertions and matchers, building custom assertion helpers, and handling exceptions effectively. It also explores cross-step validation, console and JUnit outputs, and creating custom reporting formatters. By the end, Behave’s reporting capabilities are shown as a powerful way to deliver accurate feedback, track results, and communicate quality across teams with confidence.

Lesson 3.0 – API Test Using Python, Behave Framework & GitHub Copilot focuses on mastering the craft of building powerful API test cases across multiple technologies. The lesson begins with a sample test repository and progresses through real-world examples, including REST test cases with Finnhub, SOAP tests with a temperature conversion API, GraphQL queries with the Rick & Morty API, WebSocket interactions using Bitstamp, and RPC calls with the Ankr ETH API. Each test demonstrates how Python, Behave, and GitHub Copilot streamline the process, making automation faster, smarter, and more effective. By the end, API testing is elevated from basic scripts to professional-grade workflows ready for real-world applications.

Lesson 3 – Module Guide serves as a step-by-step companion for building API test cases with Python, Behave, and GitHub Copilot. It consolidates links, setup instructions, and sample repositories for REST, SOAP, GraphQL, WebSocket, and RPC test cases. With clear examples, structured exercises, and reference materials, the guide ensures smooth progress through the lesson while reinforcing best practices in test automation. Designed as both a quick reference and a practical playbook, it keeps focus on applying skills directly to real-world API testing scenarios.

Lesson 3.1 – Clone & Setup Sample Test Repository covers the essentials of getting started with the course’s sample project. The process includes accessing and cloning the repository, exploring the structure in VS Code, and installing required dependencies. With Docker integration and an initial environment setup, the lesson ensures test cases can run smoothly from the start. By the end, the environment is fully prepared, allowing focus on building and executing API tests without setup roadblocks.

Lesson 3.2 – REST API Test Cases Using Finnhub demonstrates how to design, run, and expand REST API test cases in a real-world context. The lesson begins with an overview of the Finnhub API and API key setup, then explores sample test cases to understand their structure and anatomy. Tests are executed, outputs analyzed, and the suite expanded with new scenarios generated using GitHub Copilot in VS Code. Best practices for REST API testing with Behave tie it all together, showcasing how to build maintainable, professional-grade automation.

Lesson 3.3 – SOAP API Test Cases Using Temperature Conversion introduces the process of testing SOAP APIs through a practical, real-world example. The lesson begins with an overview of SOAP and the Temperature Conversion API, then moves into exploring sample test cases and understanding their structure. Tests are executed and results analyzed, while new feature scenarios are written and extended with the help of GitHub Copilot in VS Code. Best practices for SOAP API testing tie everything together, providing both a solid foundation and practical strategies for working with XML-based services.

Lesson 3.4 – GraphQL Test Cases Using Rick & Morty API explores how to test GraphQL APIs with Behave and Python through a fun, real-world example. The lesson begins with an overview of GraphQL and the Rick & Morty API, followed by exploring sample test cases and understanding their structure. Tests are executed, results analyzed, and new feature scenarios written and extended with the support of GitHub Copilot. Best practices for GraphQL API testing highlight how to handle flexible queries and validations, making automation both powerful and adaptable.

Lesson 3.5 – WebSocket Test Cases Using Bitstamp API highlights how to test real-time APIs with Behave and Python. The lesson begins with an overview of WebSockets and the Bitstamp API, then walks through locating and analyzing sample test cases to understand their structure. Tests are executed, outputs reviewed, and new feature scenarios created and extended with GitHub Copilot. Best practices for WebSocket API testing are emphasized, showing how to handle continuous data streams and ensure reliable validation in fast-moving environments.

Lesson 3.6 – RPC Test Cases Using Ankr ETH API demonstrates how to work with Remote Procedure Call (RPC) APIs through a real-world blockchain example. The lesson begins with an overview of RPC and the Ankr Ethereum API, then moves into locating and exploring sample test cases to understand their structure. Tests are executed, outputs analyzed, and new feature scenarios written and extended with GitHub Copilot. Best practices for RPC API testing are emphasized, highlighting strategies for reliability and accuracy when working with decentralized systems.

Lesson 4.0 – Advanced Test Architecture Overview focuses on building scalable, maintainable Behave test suites using advanced techniques. Key topics include tagging, fixtures, and hooks to streamline setup and teardown, as well as parameterized steps and data-driven testing to maximize reusability. The lesson also introduces parallel execution for faster test runs, debugging strategies for resolving failures, and an advanced topics quiz to reinforce learning. By the end, Behave test architecture is elevated from simple test cases to a robust, professional-grade framework capable of supporting enterprise-level automation.

Lesson 4 – Module Guide acts as a roadmap for applying advanced Behave testing techniques. It brings together resources, setup instructions, and sample code covering tagging, fixtures, hooks, parameterized steps, data-driven testing, and parallel execution. Designed as both a quick reference and a hands-on playbook, the guide helps streamline complex test architecture, reinforces debugging strategies, and supports the advanced topics quiz. With clear examples and best practices, it ensures advanced concepts can be applied consistently to build scalable and maintainable automation frameworks.

Lesson 4.1 – Tagging, Fixtures & Hooks using Python/Behave explores how to control and streamline test execution with Behave’s advanced organizational features. Tags allow you to group and filter scenarios, while fixtures enable reusable setup logic and shared context. Hooks manage automated setup and teardown, reducing repetition and keeping tests clean. The lesson also demonstrates how to combine tags and hooks for dynamic test flows, ensuring flexibility and efficiency. By the end, you’ll be able to design well-structured, maintainable Behave test suites that scale with your projects.

Lesson 4.2 – Test Setup / Teardown using Python / Behave shows how to take control of your test environments like a pro. Instead of fighting with brittle test data and inconsistent runs, discover how Behave’s powerful hooks simplify setup and cleanup so every scenario starts fresh and ends clean. Learn how to share context data across tests, isolate features for reliable execution, and manage resources with confidence. With these strategies, test suites become faster, more consistent, and enterprise-ready — giving teams the stability they need to ship high-quality software at speed.

Lesson 4.3 – Parameterized Steps Using Python/Behave demonstrates how to make test automation smarter, cleaner, and more scalable. Parameterized steps allow test logic to be reused across multiple inputs, reducing duplication and keeping scenarios lean. This lesson highlights how to handle dynamic inputs, improve modularity, and embrace data-driven testing for stronger coverage. With a focus on readability and scalability, it shows how to streamline assertions and validations while building flexible, future-proof test suites. By the end, test engineers gain the ability to craft reusable, professional-grade Behave steps that accelerate automation efficiency.

Lesson 4.4 – Parallel Execution Using Python/Behave demonstrates how to dramatically speed up test runs by running scenarios in parallel. Since Behave doesn’t natively support parallelism, the lesson explores practical tools like behave-parallel and concurrent.futures to overcome this limitation. Key strategies include isolating context objects to avoid shared state, executing features in separate processes, and merging results for clean reporting. The lesson also covers integrating parallel runs directly into CI/CD pipelines and evaluating when parallelism delivers true value. By the end, test execution evolves from linear and time-consuming to scalable and high-performance — a critical skill for modern automation engineers.

Lesson 4.5 – Test Debugging Using Python / Behave & VS Code unlocks the ability to troubleshoot automation like a pro. This lesson shows how to debug Behave tests efficiently in VS Code with breakpoints, launch.json customization, and targeted test execution. You’ll learn how to inspect context data, view logs, and track exceptions with clarity while running only the tests that matter. Finally, you’ll explore extensions and strategies that boost debugging speed, making failure analysis faster and smarter. With these skills, debugging stops being a pain point and becomes a powerful part of your testing workflow.

Lesson 5.0 – CI/CD Integration & GitHub Actions Overview unlocks the power of continuous integration and delivery by automating workflows with GitHub Actions. This lesson covers how YAML pipelines drive repeatable builds, branching strategies, and artifact caching to accelerate delivery. Secure secrets management and Docker integration keep pipelines reliable and safe, while parallel and matrix builds supercharge performance. Real-world examples illustrate how to streamline testing and deployment, capped off with a CI/CD quiz to solidify key concepts. By the end, GitHub Actions transforms testing pipelines into automated, enterprise-grade workflows that scale with confidence.

Lesson 5 – Module Guide lays out a framework for mastering CI/CD integration with GitHub Actions. It combines resources, setup instructions, and sample YAML pipelines covering workflows, branching, environment secrets, artifacts, caching, and matrix builds. Designed as both a quick reference and a hands-on playbook, the guide streamlines automation setup, reinforces best practices, and supports the CI/CD quiz. With practical examples and actionable guidance, it ensures CI/CD concepts can be applied consistently to build reliable, enterprise-ready pipelines.

Lesson 5.1 – Automated Workflow & Branching Using GitHub Actions introduces strategies for streamlining CI/CD pipelines with branch-aware automation. Topics include branching strategies, event-based triggers, and branch-specific workflows that align deployments with development stages. The lesson also covers workflow reusability, conditional steps for smarter automation, and environment-specific deployment mapping. By applying these techniques, CI/CD pipelines become more efficient, maintainable, and adaptable to evolving team workflows.

Lesson 5.2 – YAML-Based Pipelines & GitHub Actions explores how to define CI/CD workflows declaratively using YAML for clarity and maintainability. Topics include structuring pipelines, branch-specific workflows, and conditional steps that adapt execution to real project needs. The lesson also highlights the role of secrets and variables in powering secure automation, as well as strategies for organizing multi-job workflows across environments. With best practices for scalable pipeline design, YAML-based workflows become both flexible and professional-grade, ready to support advanced CI/CD automation.

Lesson 5.3 – Artifacts & Caching demonstrates how to make CI/CD pipelines faster, more reliable, and easier to debug by leveraging artifacts and caching in GitHub Actions. You’ll see how to run tests consistently with Docker, generate structured reports, and capture detailed logs for traceability. The lesson covers uploading reports and logs as artifacts for team collaboration, as well as using caching to dramatically reduce workflow execution times. By the end, you’ll have the skills to build CI/CD pipelines that are not only efficient but also maintain clear visibility into every test run.

Lesson 5.4 – Parallel & Matrix Builds explores how to supercharge GitHub Actions workflows with simultaneous job execution. The lesson introduces strategies for running jobs in parallel, applying matrix builds to cover multiple environments, and reducing duplication for cleaner pipelines. Key techniques include expanding test coverage, leveraging the fail-fast option for efficiency, and combining matrix execution with caching and artifacts to maximize speed. By mastering these practices, CI/CD pipelines achieve faster feedback loops, stronger coverage, and greater scalability for modern engineering teams.

Lesson 5.5 – Environment Secrets & Docker highlights how to secure sensitive data within GitHub Actions while seamlessly integrating with Docker. Topics include storing API keys and credentials safely, injecting secrets into workflows and Docker commands, and avoiding the pitfalls of hardcoding. The lesson also explores environment-based secret scoping, strategies to limit exposure, and best practices for keeping pipelines secure. With these techniques, teams can confidently manage secrets, protect critical data, and maintain compliance without sacrificing automation speed or flexibility.

Lesson 6.0 – Automating API Tests Using Python/Behave & GitHub Actions brings everything together into a fully automated CI/CD pipeline. The lesson demonstrates how to organize tests with tags and hooks, manage branching strategies and pull requests, and secure pipelines with environment secrets. It also covers generating test reports and artifacts, running matrix and parallel jobs for speed, and applying debugging strategies to resolve failed runs. By integrating Behave API tests directly into GitHub Actions, this lesson transforms manual test execution into a seamless, automated process that ensures reliability, efficiency, and continuous delivery at scale.

Lesson 6 – Module Guide brings together everything needed to automate API testing end-to-end. It includes practical resources, environment setup, and ready-to-use examples for Behave and GitHub Actions. Inside, the guide explores organizing tests with tags and hooks, managing secrets securely, capturing reports and artifacts, and scaling execution with parallel and matrix jobs. It also walks through branching strategies, pull request workflows, and debugging failed runs. More than a reference, it serves as a hands-on companion that turns API automation into a streamlined, CI/CD-ready practice.

Lesson 6.1 – Organize Tests with Tags & Hooks equips you with the skills to structure and streamline your Behave tests for maximum efficiency. You’ll learn how to group and manage scenarios with tags, establish conventions for consistency, and build hooks that simplify setup and teardown. The lesson progresses from basic hooks to advanced tag-specific logic, giving you fine-grained control over test execution. By the end, you’ll be able to run and verify tests by tag with confidence—making your suite more organized, scalable, and ready for real-world automation workflows.

Lesson 6.2 – Branching & Pull Requests shows how to align your CI/CD pipelines with modern development practices through smart branching strategies and effective pull request workflows. You’ll explore the purpose of branching, adopt naming conventions that keep projects consistent, and implement pull requests as quality gatekeepers. The lesson also covers collaboration best practices, communication tips, and how to ensure merge readiness. By mastering these techniques, you’ll streamline code reviews, boost team efficiency, and keep deployments stable and predictable.

Lesson 6.3 – GitHub Actions & Environment Secrets explores how to securely manage sensitive data like API keys in your CI/CD pipelines. You’ll learn how to configure environment secrets in GitHub, reference them inside workflows, and inject them into Docker commands without exposing credentials. The lesson also walks through reviewing YAML workflow files, triggering actions on pull requests, and applying best practices for secret management. By mastering these techniques, you’ll build automation pipelines that are not only powerful but also safe, scalable, and enterprise-ready.

Lesson 6.4 – Test Reports & Artifacts highlights how to gain visibility and insight into your automation results. You’ll explore common reporting formats, generate reports seamlessly in CI/CD, and capture artifacts for long-term storage and easy access. The lesson also demonstrates strategies for analyzing results, spotting trends, and sharing reports with your team to drive faster, data-backed decisions. By mastering test reports and artifacts, you’ll ensure that your CI/CD pipeline delivers not just automation, but actionable insights that keep your projects on track.

Lesson 6.5 – Matrix Builds & Parallel Jobs unlocks the full power of CI/CD by teaching you how to run tests faster and smarter across multiple environments. You’ll learn how to configure cross-environment testing with matrix strategies in YAML, execute jobs in parallel for rapid feedback, and selectively run only what matters. The lesson also covers artifact collection across jobs and proven techniques to optimize efficiency at scale. By mastering these techniques, you’ll transform your pipelines into high-performance systems that deliver speed, accuracy, and confidence in every release.

Lesson 6.6 – Debugging Failed Runs focuses on turning CI/CD failures into opportunities for faster, smarter fixes. You’ll learn how to dig into logs, reproduce issues locally, and use debug mode to uncover hidden problems. The lesson also explores workflow change analysis, isolating failing jobs for quicker triage, and spotting common failure patterns before they slow your team down. By mastering these strategies, you’ll transform debugging from a time-consuming chore into a streamlined process that keeps your pipeline running smoothly.

The Course Project – Automating Weather API Tests with Python/Behave & GitHub Actions is where everything comes together. ? Fork a ready-to-go test repository, connect it to a live Weather API, and put your automation skills into action by writing Gherkin feature files and Behave step definitions. With a few VS Code tweaks and GitHub Actions powering your CI/CD, the result is a fully automated pipeline running in the cloud. By the end, this isn’t just practice — it’s a showcase-worthy project that proves your skills and makes your portfolio shine. ?

The Course Project Guide is a streamlined companion designed to kickstart the final project. Instead of walking through every detail, it pulls together the key resources, setup links, and reminders needed to get started quickly. By this stage, the core skills are already mastered — the guide simply provides direction, keeps the workflow organized, and ensures nothing is missed. It’s the final push that helps turn course knowledge into a polished, showcase-ready automation project.

Thank You!

Thanks for joining the journey into API testing with Python, Behave, VS Code, and GitHub Copilot. By completing this course, you’ve gained the skills to design, build, and automate real-world API test frameworks — and a professional-grade project to showcase your expertise. Keep practicing, keep building, and most importantly, keep leveling up your testing game.