AI Code Review & Legacy Modernization with GitHub Copilot

Have you inherited a legacy codebase and are unsure where to begin? This course teaches you how to use GitHub Copilot and other AI coding assistants to understand unfamiliar code, identify bugs, security risks, and technical debt, generate protective tests, refactor safely, review pull requests, and plan a controlled modernization.

AI-Assisted Legacy Code Review and Modernization teaches a repeatable, review-first workflow for understanding, testing, improving, and modernizing inherited software. You will use AI as an investigation and implementation assistant while keeping evidence, validation, and engineering judgment at the center of every decision.

This course isn't about asking AI to rewrite a legacy application and hoping the result works. The focus is disciplined software engineering: establish a baseline, use AI to accelerate investigation, verify findings against the source code, generate protective tests, review every change, and document the evidence needed before modernization work is accepted.

The workflow is designed to transfer across programming languages, frameworks, and AI coding tools. The demonstrations use GitHub Copilot and a legacy C#/.NET web application so that the course can examine a real system in depth. The same methods can be adapted to Java, JavaScript and TypeScript, Python, PHP, C++, and other development stacks by changing the project-specific files, build commands, test tools, and modernization targets.

The course also prepares you for more agentic modernization workflows, where AI tools can help assess a codebase, propose a plan, make changes, and prepare pull requests. You will learn why those workflows still require human review, scoped tasks, validation evidence, rollback thinking, and clear acceptance criteria before changes are merged.

You will begin by establishing a baseline. Before asking AI to refactor or modernize anything, you will get the inherited application running, record its structure, document the environment, identify key workflows, and capture what currently works. This provides a stable comparison point for later changes.

Next, you will configure AI for serious review work. You will create repository instructions, reusable prompt files, and focused review prompts that define scope, required evidence, output format, and stop conditions. These patterns help reduce generic responses and make AI findings easier to verify.

You will then use AI to understand the codebase. You will summarize the application's structure, trace a standard workflow, follow a cross-cutting workflow, and identify hidden business rules. The goal is not to produce documentation for every file. It is to capture sufficient verified knowledge to make safe decisions about review and modernization.

The risk-triage section turns observations into actionable engineering work. You will examine technical debt, security and privacy concerns, access-control assumptions, dependency risks, and modernization readiness. Each finding is separated into evidence, impact, severity, confidence, and recommended action for another developer or decision-maker to review.

Testing is treated as modernization evidence. You will use AI to propose and generate test scenarios, then critically review whether those tests actually protect current behavior. You will work with characterization, unit testing, integration testing, and manual validation strategies before allowing refactoring or migration to proceed.

The refactoring workflow helps you decide whether a change should be implemented as a small structural refactor, a controlled rewrite, a deferred item, or an investigation. You will validate changes against the baseline and tests, rather than accept an AI-generated improvement just because it looks cleaner.

You will also learn how to communicate the work. The course covers AI-assisted pull request preparation, PR review, validation evidence, risk notes, and review packages that help teammates understand what changed and what still requires attention.

Finally, you will treat modernization as another form of code review. You will evaluate an AI-generated assessment, inspect the proposed plan, execute changes in controlled checkpoints, review the resulting diff and behavior, and use a fallback workflow when a dedicated modernization agent is unavailable.

By the end of the course, you will have a reusable process for approaching inherited systems with less guesswork. You will be able to use AI to accelerate codebase understanding, risk discovery, test planning, refactoring, review communication, and modernization while remaining responsible for what is accepted, merged, and maintained.