AI辅助遗留代码审查与现代化：用GitHub Copilot安全重构 – CG群

掌握AI辅助遗留代码审查与现代化技术，利用GitHub Copilot等工具系统化发现漏洞、技术债与风险。本课程基于C#/.NET真实场景，教授“审查先行”的工程方法，帮助你安全重构遗留系统并建立标准化升级流水线。

原始标题：AI-Assisted Legacy Code Review and Modernization

AI-Assisted Legacy Code Review and Modernization

（AI-Powered Code Review: Find Bugs, Debt, and Risks） 是一门专为开发者及技术团队打造的实用进阶课程，旨在教导学员如何将 GitHub Copilot 等 AI 工具转化为理清、归类并安全重构遗留代码（Legacy Code）的专业审查伙伴。课程基于 C# 和 .NET / ASP.NET Core 的真实场景演练，全方位颠覆了“只求速度、盲目生成”的传统 AI 编码套路，转而倡导“审查先行（Review-first）”和“通过测试构建基准线（Baseline）”的严谨工程方法。在这一框架下，学员将系统化地学习如何构建可复用的高级提示词库，利用 AI 跨文件挖掘隐藏在控制器或视图中的陈旧业务规则、安全漏洞及权限假设，并建立包含证据、影响力和严重级别的技术债清单。

同时，课程还深入讲解了如何借助 AI 补齐单元测试、集成测试以形成安全的重构“代码缝隙”，评估并修正 AI 导出的现代化迁移方案，以及如何输出标准化的 PR 摘要与团队审查交接包，最终帮助学员在保持人类工程师绝对控制权与核心评判力的前提下，建立一套标准化、可复制的 AI 辅助遗留系统安全升级流水线。

Use GitHub Copilot to review legacy code, document behavior, generate tests, triage risks, and modernize safely.

What you’ll learn
⚡ Use AI tools such as GitHub Copilot to support structured code review work.
⚡ Review inherited codebases efficiently using AI assistance
⚡ Create reusable prompts for code review, feature review, risk triage, and modernization planning.
⚡ Summarize code structure and trace feature workflows with AI assistance.
⚡ Identify hidden business rules, role assumptions, validation behavior, and undocumented workflow expectations.
⚡ Build a technical debt register that separates evidence, impact, severity, and recommended action.
⚡ Use AI to identify security, privacy, access control, dependency, and modernization readiness concerns.
⚡ Generate and review unit, integration, browser-level, and manual tests.
⚡ Refactor small code seams safely using AI assistance.
⚡ Prepare useful pull request summaries and AI-assisted review packages.
⚡ Review AI-generated modernization plans before accepting, revising, or rejecting them.
⚡ Apply AI responsibly by verifying findings through code, tests, behavior, and human judgment.

Requirements
❗ Basic C# and .NET experience.
❗ Basic familiarity with ASP.NET Core MVC or web application structure is helpful.
❗ Basic Git and GitHub knowledge.
❗ Ability to open, build, and run a .NET project using Visual Studio, Visual Studio Code, Rider, or the .NET CLI.
❗ Access to GitHub Copilot or a similar AI coding assistant is recommended for hands-on practice.
❗ No advanced AI, cybersecurity, or legacy modernization experience is required.

Description
AI coding tools can generate suggestions quickly. Professional code review still depends on context, evidence, testing, and judgment. When you inherit an older application, the risky parts are rarely obvious from the first file you open. Business rules may be hidden in controllers, views, services, JavaScript, validation logic, database assumptions, role checks, and years of small maintenance decisions.

AI-Powered Code Review: Find Bugs, Debt, and Risks is designed to help you use AI as a practical review partner while keeping you in control of the engineering decisions. You will learn how to review an inherited application using a structured workflow that helps you understand the existing system before making changes, identify meaningful risks before they become production problems, and modernize code with better evidence and less guesswork.

This course is for developers who already know AI can help write code but want a stronger process for code review. Many AI coding workflows focus on speed: generate a feature, accept a change, move on. That approach can be dangerous when the codebase already has hidden behavior, weak tests, unclear ownership, outdated dependencies, inconsistent validation, or fragile business workflows. In this course, you will practice a review-first approach that treats AI output as a starting point for investigation rather than a final answer.

You will work through a realistic inherited application scenario and learn how to build a baseline before making changes. That baseline matters. Before refactoring, rewriting, or modernizing a feature, you need to know what currently works, what assumptions the system depends on, what user roles are expected to do, and where behavior might break. The course walks you through environment setup, database setup, project execution, and baseline assessment, so your review begins with working evidence rather than assumptions.

From there, you will configure AI for serious review work. You will explore GitHub Copilot review features, repository instructions, reusable prompt libraries, and structured review prompts that produce more useful output. The goal is to make AI responses easier to verify, compare, and turn into review artifacts. You will learn how to ask for focused analysis of features, workflows, technical debt, dependencies, security concerns, and modernization readiness, rather than relying on broad prompts that yield generic feedback.

A major part of the course focuses on understanding the legacy codebase. You will use AI to summarize the project’s structure, trace existing behavior and workflows, and uncover hidden business rules. This is where AI can be especially useful: not by magically understanding the entire system for you, but by helping you inspect more code paths, ask better questions, and document behavior faster. You will learn how to capture what you find in a form that another developer, reviewer, or technical lead can actually use.

Once the application is understood, you will move into risk triage. You will practice using AI-assisted review to build technical debt snapshots, inspect security and privacy concerns, review role and access-control assumptions, evaluate dependency risks, and assess modernization readiness. The emphasis is on separating evidence from opinion. A useful code review does not simply say that code is messy. A useful review explains what was found, why it matters, how severe the risk appears to be, and what action should be considered next.

Testing receives special attention because tests provide the confidence needed for safe refactoring and modernization. You will learn how to use AI to propose unit tests, integration tests, browser-level checks, smoke tests, workflow checks, and manual validation steps. You will also learn how to critically review those AI-generated test ideas. Weak tests can create false confidence, so the course shows how to think about test gaps, characterization tests, validation evidence, and the difference between generated test code and meaningful coverage.

The refactoring section helps you decide where AI can support safe improvement. You will look for small code seams such as repeated lookup setup, mapping logic, helper methods, validation organization, and other areas where controlled refactoring can reduce risk. You will also examine when a controlled rewrite may be safer than repeatedly patching fragile code. The course does not encourage reckless modernization. The workflow keeps the baseline, review notes, tests, and decision-making process visible before changes are accepted.

You will also learn how to package review work for real team workflows. The course includes AI-assisted pull request preparation, pull request review, and reusable review packages. This matters because code review is rarely a private exercise. Your findings need to be communicated clearly to teammates, reviewers, project owners, or future maintainers. You will practice turning AI-assisted analysis into useful summaries, evidence-based recommendations, and review artifacts to support practical maintenance decisions.

The modernization section brings the workflow together. You will frame modernization as another form of code review, evaluate AI-generated modernization plans, approve or revise proposed changes, review migrated behavior, and learn how to continue modernization even when a dedicated modernization agent is unavailable. The course helps you think like a responsible reviewer: understand the current system, verify the proposed path, test the result, and protect business behavior.

By the end of the course, you will have a repeatable process for AI-assisted code review that you can adapt to real inherited projects. You will be able to use AI tools more deliberately to understand code, identify likely bugs, document hidden behavior, identify technical debt, review security and access control concerns, generate better test ideas, prepare PR summaries, and approach modernization with more confidence.

You do not need to be an AI expert, modernization specialist, or cybersecurity professional. You should be comfortable reading and running application code and willing to verify AI findings rather than accept them unquestioningly. If you have ever opened an inherited project and wondered where to begin, this course offers a practical path. You will leave with more than prompt examples. You will leave with a professional review habit for AI-assisted development: use AI to accelerate investigation, then use your engineering judgment, tests, and evidence to decide what deserves to change.

Who this course is for
⭐ Developers who want to use AI tools for practical code review work.
⭐ Developers inheriting legacy applications who need a safe way to understand code before changing it.
⭐ Senior developers and technical leads who need lightweight review packages for PRs, modernization planning, and team handoffs.
⭐ Developers preparing to modernize older applications while protecting existing behavior with tests and evidence.
⭐ This course is not designed for absolute beginners who have never read or run application code.

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