Learn to accelerate your software development workflow by combining GitHub Copilot with test-driven development, system-wide refactoring, and infrastructure-as-code generation. This course teaches you to use AI assistance at every stage of code quality — from writing your first test to deploying containerized applications.
You will start with AI-assisted test-driven development, using GitHub Copilot to generate test cases, mock dependencies, and evaluate test coverage with pytest. You will then move to system-wide refactoring, leveraging @workspace references to analyze cross-file dependencies, enforce coding standards, and execute coordinated code cleanup across large codebases.
The course concludes with infrastructure-as-code generation, where you use Copilot to produce Ansible playbooks, Dockerfiles with distroless multi-stage builds, and Terraform configurations for cloud deployment. Each lesson includes hands-on challenges and solution walkthroughs using real Rust and Python projects.
By the end of this course, you will have a practical toolkit for integrating AI assistance into testing, refactoring, and infrastructure workflows — skills that directly reduce development cycle time while improving code quality.
Overview
Syllabus
- AI-Assisted Test-Driven Development
- Covers AI-assisted TDD fundamentals, generating complex test suites, mocking dependencies, hands-on TDD challenges, and evaluating test coverage with GitHub Copilot.
- System-Wide Refactoring and Infrastructure as Code
- Covers strategic workspace usage, cross-file dependency analysis, system-wide code cleanup, style enforcement, custom guidelines, infrastructure-as-code generation with Dockerfiles and Terraform, and course conclusion.
- Capstone — AI-Augmented Development in Practice
- Apply AI-assisted testing, system-wide refactoring, and infrastructure-as-code generation techniques in an end-to-end development scenario that synthesizes all course concepts.
Taught by
Alfredo Deza