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AI Code Review & Legacy Modernization with GitHub Copilot

Name: AI Code Review & Legacy Modernization with GitHub Copilot
Rating: 4.6 (12 reviews)

Use AI coding assistants to review legacy code, uncover risks, generate tests, refactor safely, and plan modernization.

Created byTrevoir Williams

Last updated 7/2026

English

What you'll learn

Use AI coding assistants to support structured, evidence-based legacy-code review.
Establish a runnable baseline before changing an inherited application.
Summarize unfamiliar codebases and trace standard and cross-cutting workflows.
Identify hidden business rules, role assumptions, validation behavior, and undocumented dependencies.
Separate verified evidence from AI-generated assumptions and recommendations.
Build a technical debt and risk register with impact, severity, confidence, and recommended action.
Build a technical debt and risk register with impact, severity, confidence, and recommended action.
Generate and critically review characterization, unit, integration, and manual validation scenarios.
Refactor small code seams safely and decide when a controlled rewrite is more appropriate.
Prepare clear pull request summaries, reviewer notes, and reusable review packages.
Validate changes through source inspection, builds, tests, runtime behavior, and diff review.
Adapt the workflow to other languages, frameworks, repositories, and AI development tools.

Course content

11 sections • 71 lectures • 4h 25m total length

Introduction3:06
Explore AI powered code modernization as a professional engineering workflow, using GitHub Copilot to assess inherited systems, preserve behavior, validate recommendations, and plan safe migrations to ASP.NET Core MVC.

Section Overview1:36
Adopt a review-first mindset for responsible AI assisted legacy modernization. Review business context, assess modernization risks, integrate GitHub Copilot, and follow an evidence-based, human-owned end-to-end workflow.
School Management Application Scenario2:27
Learners understand enough business context to review the code responsibly.
What Makes a Legacy Codebase and Modernization Risky?3:39
Learners can explain why reviewing an inherited codebase requires structure.
What a Professional Modernization Should Cover3:49
Learners have a review framework before introducing AI.
Where AI Fits in Legacy Code Review5:03
Learners understand AI as a review accelerator, not as a replacement for a reviewer.
Responsible AI Usage for Code Review1:09
Learners understand that AI findings must be verified.
The End-to-End Review Workflow0:54
Learners understand how the course progresses.
Defending a Review-First Modernization Approach
Section Review0:51
Ai accelerates modernization assessment, but engineers own the decisions; inherited apps require review as platform and business risks intertwine, with behavior, data, security, dependencies and validation evidence in Copilot workflow.

Section Overview0:41
Clone and restore the school management legacy project, verify build and run, ensure database connectivity, review configuration files, and create a baseline assessment checklist to establish the modernization starting point.
Section Source Code
Configure Environment and Development Tools [Demo]6:16
Learners setup their development environment and tools
Clone and Open the Legacy Project [Demo]2:53
Learners can open the inherited application.
Setup Local Development Database [Demo]5:46
Learners understand how to test role-based flows in the course.
Build and Run the Sample Project [Demo]4:22
Learners confirm the application runs before review work begins.
Create the Baseline Assessment Checklist [Demo]5:43
Learners establish a baseline before making changes.
Section Review1:38
Confirm that modernization starts only after the inherited baseline is known and documented.
Confirming the Legacy Baseline Before AI Assistance

Section Overview0:50
Explore configuring GitHub Copilot for modernization with repeatable, evidence-backed reviews aligned to your repository. Learn to use the modernization prompt library and reusable prompts in a structured workflow.
Section Source Code
GitHub Copilot Review Features Overview3:58
Learners know which Copilot features support review workflows.
Review the Repository Copilot Instructions5:09
Learners understand how repository instructions improve AI consistency.
Create a Code Review Prompt Library [Demo]9:26
Learners have reusable prompts for recurring review tasks.
Write a High-Quality Review Prompt [Demo]4:52
Learners can write prompts that produce reviewable AI output.
Create a Feature Review Prompt [Demo]10:13
Demonstrates creating a reusable feature review prompt for a legacy MVC app, detailing focus areas, risks, and validation, with an enrollments feature example and a baseline template.
Section Review1:03
Leverage AI code review to drive legacy modernization by prioritizing project context, using repository instructions to reduce drift, and building reusable prompts with task, context, constraints, output format, and validation.
Writing Guardrails for AI Review Prompts

Section Overview0:52
Use AI to understand and document legacy applications before modernization, trace end-to-end feature workflows, identify business and role rules, and generate architecture and behavior nodes for a feature orientation report.
Section Source Code
Ask AI to Summarize the Codebase [Demo]11:04
Use AI to summarize the solution structure. Learners produce a high-level codebase summary.
Trace a Standard CRUD Workflow [Demo]8:34
Trace a standard CRUD workflow in the courses management feature, covering retrieve, create, edit, and delete operations, authorization, validation, and data persistence through the DB context.
Trace a Cross-Cutting Workflow [Demo]13:52
Review enrollment creation, dropdown data, partial views, AJAX behavior, and student search behavior.
Identify Hidden Business Rules [Demo]9:57
Use AI to surface role assumptions, access rules, validation behavior, workflow expectations, and missing documentation.
When to Stop Documenting and Start Reviewing6:43
Learners document one workflow before reviewing or changing it.
Explaining Legacy Workflows Without Over-Documenting
Section Review1:11
Reinforce that understanding current behavior comes before modernization.

Section Overview0:55
Leverage an ai assistant to triage legacy code risks, create concise technical debt snapshots, and build a risk register covering security, privacy, and access control concerns, to guide modernization decisions.
Section Source Code
From Understanding to Risk Triage1:00
Transition from understanding to risk triage for an inherited application, using ai to group findings, compare impact, and drive evidence-based decisions with code configuration or documentation.
Technical Debt Snapshot [Demo]5:26
Assess technical debt in a legacy mvc app, including a direct EF6 context in controllers, fat controllers, and decide what to fix now for modernization readiness via dependency injection.
Security, Privacy, and Access Control Snapshot [Demo]5:05
Examine security and access control in a code review with GitHub Copilot, highlighting authorization gaps, anti-forgery risks, role validation flaws, and data exposure, plus a triage workflow for security concerns.
Dependency and Modernization Readiness Snapshot [Demo]2:28
Assess dependency modernization with a readiness snapshot to mitigate risks from out-of-support packages, migration from mvc5 to asp.net core mvc on net 10, including entity framework six and identity two.
Build the Consolidated Tech Debt Register [Demo]4:01
Demonstrates building a consolidated legacy risk triage register for incremental modernization by grouping findings into technical debt, security, dependencies, and validation gaps, with evidence, risks, severity, and checkpoint-based decisions.
Section Review0:55
Create a triage artifact that flags what to fix now, what to defer, and what to validate during migration, then set a maintenance and security priority list before AI-driven modernization.
Building a Decision-Ready Risk Register

Section Overview0:37
Generate and refine tests with AI, prioritizing test coverage before refactoring, identify legacy characterization scenarios, and plan a safety net to protect inherited behavior before risky changes.
Section Source Code
Why Tests Come Before Refactoring and Modernization1:31
Explain characterization tests, smoke tests, workflow checks, and validation evidence.
Generate and Review Test Ideas with AI [Demo]7:31
Use AI to propose unit, integration, browser-level, or manual validation checks.
Generate and Review Unit Tests [Demo]6:40
Create and review a legacy compatible ms test project for an inherited mvc app, using arrange-act-assert, metadata and anti-forgery baseline tests, and ensure all tests pass.
Generate and Review Integration Tests [Demo]6:39
Generate and review integration tests using a dedicated local test database, ensuring deterministic, non-parallelized tests, with infrastructure setup, test data factory, and controller-level integration validation.
Guided Lab - Generate Test Gap Report [Optional]6:05
Section Review1:10
Summarize testing as evidence for safe modernization.
Challenging AI-Generated Test Ideas

Section Overview1:18
Explore a maintenance workflow for legacy applications, using AI to assist in safe refactoring rather than rewriting, and learn to scope options for small, reliable code changes.
Section Source Code
When a Controlled Rewrite Is Safer Than Refactoring2:50
Clarify the difference between safe refactoring and risky rewriting.
Refactor Small Code Seams Safely5:53
Identify safe seams such as repeated lookup setup, mapping, helper methods, or validation organization.
Rewriting a Module With AI-Assistance11:11
The lecture demonstrates rewriting a registration module with AI assistance to remove user-selected roles, assign a default role, and harden security, supported by plan-driven changes, tests, and minimal reversible options.
Deciding What to Refactor or Rewrite1:38
Explore how to decide what to refactor or rewrite in a legacy app by defining code smells, evidence, proposed changes, validation checks, and when to defer modernization using AI-assisted drafting.
Section Review1:45
Summarize how small, validated refactorings reduce risk before migration.
Refactor or Rewrite: Choosing the Safer Change

Section Overview0:39
Prepare and merge changes into the code base using AI-assisted pull requests, and craft reviewer notes, a reviewer checklist, and an AI-assisted review package that communicates intent, risk, and validation.
Section Source Code and Reference PR0:02
Prepare a Pull Request with AI6:00
Use AI to summarize the review branch. Learners can generate a useful PR summary.
AI-Assisted PR Review2:35
Learners apply the full workflow to one ClaimDesk feature. Learners produce a professional review package they can reuse in real projects.
Section Review1:03
Review the section's key takeaways: craft strong pull requests with clear intent, risk, and validation evidence, aided by ai-assisted drafting, while planning modernization for legacy projects.
Defending a Pull Request Review Package

Section Overview1:32
Modernize your project using the GitHub Copilot modernization agent to draft and refine a plan, review code assumptions, and stabilize build, runtime, and tests across any stack.
Modernization Is Still Code Review3:59
Frame modernization as review work. Learners understand modernization as a controlled review process.
GitHub Copilot Modernization Agent Capabilities3:39
Leverage the GitHub Copilot modernization agent to assess, plan, and execute cross-repo modernization with reusable skills, human-in-the-loop reviews, and cloud-ready migrations.
Run the GitHub Copilot Modernization Agent - Assessment6:29
Learners approve, revise, or reject the AI-generated modernization plan.
Run the GitHub Copilot Modernization Agent - Plan2:13
Outlines a 10-task plan to modernize a legacy app with GitHub Copilot, creating an asp.net core 10 web app and migrating authentication, identity, data access, and controllers.
Run the GitHub Copilot Modernization Agent - Execute7:37
Run the GitHub Copilot modernization agent to execute project creation, perform package inventory and mapping, and generate a migration plan for authentication, identity, and core upgrades.
Review and Test Migrated Application6:58
Review and test a migrated application using the GitHub Copilot modernization agent, evaluating task breakdown, data access updates, EF Core migration, identity compatibility, and bridging legacy APIs.
Modernization Without the Dedicated Agent6:15
Assess and plan modernization without a dedicated agent, using incremental slices from shell to business logic, data access, and identity layers, with evidence-backed checkpoints and reversible commits.
Section Review1:14
Maintain modernization as a review workflow, even with an agent, by verifying baseline and assumptions before execution, and using build, workflow, and test evidence to validate checkpoints.
Approving the Copilot Modernization Agent Plan

Requirements

Basic software-development experience in any programming language.
Comfort reading, navigating, building, and running an existing application.
Basic Git and source-control knowledge.
Access to an AI coding assistant is recommended for hands-on practice.
No advanced AI, cybersecurity, testing, or modernization experience is required.
The demonstrations use GitHub Copilot and a legacy C#/.NET web application. Learners using other stacks can apply the same review process by adapting project files, build commands, test tools, and modernization targets.

Description

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.

Who this course is for:

Developers inheriting an unfamiliar or poorly documented codebase.
Software engineers responsible for maintenance, technical debt, code quality, and modernization.
Senior developers and technical leads who need repeatable review artifacts for team decisions.
Architects and consultants assessing modernization readiness and migration risk.
Developers who already use AI to generate code but need a disciplined process for reviewing existing systems.
Teams adopting AI coding assistants without surrendering engineering accountability.
This course is not designed for absolute beginners who have never read, built, or debugged application code.

AI Code Review & Legacy Modernization with GitHub Copilot

What you'll learn

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Course content

Introduction1 lecture • 3min

Adopting A Review-First Mindset9 lectures • 19min

Project Setup and Legacy Baseline Review9 lectures • 27min

Configuring AI for Review and Modernization Work9 lectures • 36min

Understanding and Documenting the Legacy Codebase9 lectures • 52min

AI-Assisted Legacy Risk Triage9 lectures • 20min

Generating and Improving Tests with AI9 lectures • 30min

Safe Refactoring with AI Assistance8 lectures • 25min

Pull Requests, Review Packages, and the Maintenance Workflow6 lectures • 10min

AI-Assisted Project Modernization10 lectures • 40min

Requirements

Description

Who this course is for: