GitHub Copilot holds a wide lead for legacy code modernization across developer workflows. For automated refactoring tied to safe pull requests, Cursor is the usual answer instead.
AI can help you modernize a legacy system by accelerating code analysis, documenting undocumented business logic, refactoring difficult modules, migrating to newer frameworks, generating tests, and validating that the modernized system behaves like the original.
The most effective approach is AI-assisted, incremental modernization—not asking AI to rewrite the entire application in one step. Use AI to speed up engineering work while keeping architecture decisions, business-rule verification, security, and production releases under human control.
Current tools support much of this workflow. For example, GitHub Copilot modernization supports assessment, framework upgrades, and migrations for supported Java, .NET, and C++ applications. AWS Transform supports code transformations, dependency analysis, and modernization of mainframe and Windows workloads.
Create an initial system architecture and modernization inventory.
Deliverable: a system inventory, dependency map, and prioritized technical-debt report.
Step 2 — Refactor safely
Ask AI to improve the internal structure of existing code without changing its externally observable behavior.
Extract large functions into smaller, cohesive methods.
Identify and simplify duplicated logic.
Introduce interfaces around legacy dependencies.
Improve error handling, naming, logging, and testability.
Suggest design patterns where they address an actual problem.
Deliverable: small, reviewable code changes with automated regression tests.
Step 3 — Plan the technology migration
Use AI to compare target technologies and create a migration plan based on actual application dependencies.
Assess compatibility with newer language and framework versions.
Identify replacement libraries and unsupported APIs.
Evaluate monolith-to-modular architecture or microservices where justified.
Generate API adapters, configuration changes, container definitions, and infrastructure-as-code.
Break migration work into independently testable phases.
Deliverable: a target architecture, migration backlog, and risk register.
Step 4 — Test and validate the migration
Use AI to generate tests, identify missing scenarios, and compare the old and new implementations.
Generate unit, integration, and API contract tests.
Create characterization tests for legacy behavior.
Compare outputs from old and new implementations using the same inputs.
Generate test data and investigate failures.
Assist with performance, security, and load-test analysis.
Deliverable: evidence that the new implementation preserves required behavior and meets acceptance criteria.
Step 5 — Deploy incrementally and maintain
Use AI to assist with pull requests, deployment configuration, release notes, monitoring queries, and production issue analysis.
Migrate one component or workload at a time.
Use feature flags, canary releases, or parallel execution where appropriate.
Monitor errors, latency, data consistency, and business outcomes.
Continuously identify and remediate newly introduced technical debt.
Deliverable: a controlled transition with measurable outcomes and a rollback strategy.
2. Choose the right AI tools
The best tool depends on your existing technology stack, deployment target, and the scale of your modernization effort.
GitHub Copilot modernization
(Good for Java and .NET)
Assess application dependencies, plan upgrades, refactor code, and migrate supported applications to Azure. Particularly useful for teams working in Visual Studio or VS Code.
Documentation:
AWS Transform
(Good for enterprise migrations)
Useful for mainframe modernization, Windows and .NET workloads, dependency mapping, framework upgrades, and repeatable transformations across many repositories.
Documentation:
GitHub Copilot or another repository-aware coding assistant
(Good for targeted refactoring)
Ask the assistant to explain unfamiliar modules, generate characterization tests, propose smaller refactorings, and review pull requests. This is often the easiest way to begin without adopting a large migration platform.
Documentation:
These tools overlap, so you do not need all of them. Start with the tool that fits your repository and deployment environment, then add specialized migration tooling if the scope warrants it.
3. Practical example: modernizing a legacy application
Imagine an application with the following characteristics:
Backend: Java 8 with an older Spring framework.
Database: Oracle.
Architecture: a tightly coupled monolith.
Deployment: on-premises virtual machines.
Challenges: limited automated tests, outdated dependencies, and slow releases.
A sensible AI-assisted modernization plan would look like this:
Phase
AI-assisted task
Expected output
Discovery
Analyze packages, dependencies, database access, and call relationships
Architecture and dependency inventory
Baseline
Generate characterization tests from existing behavior
Regression test suite
Refactoring
Extract business logic from controllers and database code
More modular application
Framework upgrade
Identify breaking changes and migrate incrementally
Supported Java and Spring versions
Architecture
Define service boundaries and external API contracts
Target architecture
Infrastructure
Generate container configurations and deployment manifests
Reproducible deployment
Migration
Compare legacy and modernized outputs, performance, and errors
Verified release candidate
Important: Do not combine all these changes into one large AI-generated pull request. Upgrade the framework, change the internal structure, and alter the deployment architecture in separate, testable increments wherever possible.
For a monolith that needs gradual replacement, consider the Strangler Fig pattern: route selected functionality to new components while the remaining functionality continues to run in the legacy application. This reduces the risks of a full-system cutover.
4. Prompts you can use with an AI coding assistant
The quality of the result depends heavily on how you constrain the task. Give the AI explicit context, boundaries, and acceptance criteria.
Prompt A — Analyze the legacy codebase
[Copy]
Use this first, before allowing AI to change code.
Prompt B — Refactor a legacy module
[Copy]
Use this for one function, class, or module at a time.
Prompt C — Migrate to a new framework
[Copy]
Replace the bracketed values with your actual source and target technologies.
Prompt D — Validate old versus new behavior
[Copy]
These prompts are designed to keep AI focused on evidence, small changes, and verifiable outcomes rather than speculative rewrites.
5. Put guardrails around AI-generated changes
Legacy systems often contain undocumented behavior that the business relies on. AI may inadvertently remove such behavior while producing code that looks cleaner.
Use the following controls:
Protect sensitive data. Use approved enterprise AI tools, control repository access, and avoid sending credentials, production records, or regulated data to unapproved services.
Establish a baseline. Record current functional behavior, performance, failure rates, and critical business workflows before modifying code.
Require automated tests. Run regression, integration, security, and compatibility tests in CI.
Review dependency upgrades. Check support status, licensing, vulnerability reports, and transitive dependency changes.
Keep changes reviewable. Require human approval for architectural changes, security-sensitive code, schema changes, and production deployments.
Make rollback possible. Use versioned releases, database migration strategies, backups, and staged deployment.
GitHub's own guidance for AI-generated fixes emphasizes human review, verification of intended behavior, successful CI checks, and careful evaluation of dependency changes.
6. Measure whether AI is actually helping
Track both productivity and software quality. Faster code generation alone is not a successful modernization outcome.
Engineering productivity
Measure time spent understanding unfamiliar code, time per completed migration task, review turnaround, and rework.
Establish baseline measurements before the pilot and compare results afterward. Set targets based on your system rather than assuming AI will deliver a particular percentage improvement.
7. A practical 30-day starting plan
Week 1 — Assess
Select one representative application or module. Use AI to map dependencies, document business rules, and identify high-value modernization opportunities.
Week 2 — Establish safety nets
Add characterization tests, automate builds, establish CI checks, and select a module with a manageable dependency footprint.
Week 3 — Run a controlled pilot
Use AI to refactor or upgrade that module. Review every change, run the test suite, and record time spent and defects found.
Week 4 — Evaluate and expand
Compare results against the baseline. Document reusable prompts and coding rules, address failures, and decide whether to scale the approach to more modules.
Let's tailor this to your legacy system
The most useful next step is to identify your current technology stack and intended destination, because a COBOL mainframe migration requires a different AI strategy from a Java monolith or a legacy .NET application.
Modernization profile
Select what best describes your project. You can leave the defaults or change only the fields you know.
1. What technology does the legacy system use?
Java / Spring
.NET / C#
COBOL / mainframe
PHP / Python / Node.js
Other or mixed stack
2. What is your target?
Modernize framework and architecture
Migrate to cloud
Rewrite in a different language
Incrementally replace a monolith with services
Undecided — need an assessment
3. What is the main focus?
Code refactoring
Technology migration
Both refactoring and migration
4. Approximate system size
Small — under 10 modules
Medium — 10–100 modules
Large — 100+ modules or multiple applications
5. Constraints or additional context (optional)
[Textarea placeholder: E.g., database, cloud provider, regulatory requirements, timeline, test coverage...] (Current value unavailable.)
Modernizing a legacy system with AI requires a strategic, phased approach. Instead of a risky "big bang" rewrite, AI excels at accelerating specific, high-friction tasks like code comprehension, incremental refactoring, and translation.
Here is how you can effectively leverage AI across the modernization lifecycle:
Code Comprehension and Reverse Engineering
Documentation Generation: Use large language models (LLMs) or specialized code intelligence tools to analyze undocumented legacy codebases and auto-generate API documentation, architectural overviews, and data-flow diagrams.
Dependency Mapping: Use AI-driven static analysis tools to map out hidden dependencies, tight coupling, and "spaghetti code" paths before you touch a single line of production logic.
Refactoring and Code Translation
Automated Code Translation: Feed well-scoped legacy modules (e.g., COBOL, old Fortran, or legacy Java/C++) into specialized migration tools to translate syntax into modern languages like Java, C#, or Python.
Smell Detection and Cleaning: Utilize AI coding assistants (such as GitHub Copilot or enterprise-grade code refactoring agents) to identify anti-patterns, dead code, and security vulnerabilities, rewriting them into cleaner, modular idioms.
Writing Tests and Ensuring Parity
Unit Test Generation: Legacy systems often lack test coverage, making refactoring terrifying. Use AI to analyze existing functions and instantly generate comprehensive unit test suites (e.g., JUnit, PyTest) to lock down current behavior.
Behavioral Verification: Prompt AI to help construct golden-master or characterization tests that compare inputs and outputs between the legacy module and the newly refactored component to guarantee functional parity.
Managing Governance and Review Bottlenecks
Automated Code Reviews: As AI code generation and translation scale up, governance becomes a hurdle. Integrate AI-powered review bots to catch architectural drifts, style violations, and compliance issues early in the pull-request phase.
To tailor this strategy, could you share:
What is the legacy language/stack and the target technology?
Roughly how large is the codebase (lines of code or number of modules)?
AI tools and workflows for legacy code modernization
The most effective approach is to combine AI coding agents with deterministic refactoring tools, automated tests, and incremental migration. AI is particularly useful for understanding unfamiliar code, tracing dependencies, proposing architectural changes, and translating business logic. Rule-based tools are generally better for repetitive, predictable transformations across thousands of files.
For example, if you're migrating a legacy Java application to Spring Boot 3, modernizing a .NET monolith, or moving an older web app to a newer framework, I would not rely on a single AI tool to rewrite the entire application. I'd use a staged workflow in which every change can be tested and reviewed.
1. AI tools worth evaluating
OpenRewrite
(Best for repeatable migrations)
Best for Java and JVM modernization, framework upgrades, dependency changes, and large-scale source transformations. Its composable recipes can update code, build files, and configuration in a repeatable way. Use it before asking an AI agent to perform thousands of similar edits.
Best for repository-aware coding assistance, explaining unfamiliar modules, generating tests, proposing refactors, and implementing changes in small batches. Particularly useful when a developer needs to stay in control of each pull request.
Best for complex, multi-file refactors: tracing call chains, mapping implicit dependencies, extracting services, and reasoning about changes spanning modules. Give it a bounded task and explicit verification criteria rather than an unrestricted rewrite.
Best for enterprise modernization involving legacy platforms, cloud migration, dependency analysis, and large portfolios of applications. It supports specialized transformation workflows rather than only interactive code completion.
Worth evaluating for code explanation, test generation, repository-level assistance, and teams already invested in Google Cloud. Validate how well its available features handle your repository size and migration tasks before standardizing on it.
These tools serve different purposes, so they are not direct substitutes. In particular, OpenRewrite is a transformation engine, while coding agents are better suited to tasks requiring contextual reasoning. For .NET, TypeScript, Python, or frontend framework migrations, select language-aware codemods and analyzers alongside the AI agent.
2. The workflow that works best in practice
I recommend a six-stage process that keeps the AI focused on bounded tasks and uses automated checks as the safety net.
Inventory and understand the legacy system
Use a coding agent to map modules, entry points, dependencies, database access, external integrations, and business rules. Generate an architecture summary and identify the riskiest areas before changing code.
Build a safety net
Add characterization tests that capture what the existing application actually does. Record representative API responses, database behavior, and important edge cases. Establish a baseline for builds, tests, performance, and security.
Automate mechanical transformations
Use AST-aware codemods, migration recipes, framework upgrade tools, and linters for predictable changes. For Java, OpenRewrite is a strong option. For .NET, use the current GitHub Copilot modernization agent in supported Visual Studio versions.
Use AI for semantic changes
Have the agent handle tasks such as extracting business logic from controllers, replacing deprecated patterns with modern equivalents, introducing dependency injection, and separating persistence from application logic.
Migrate incrementally
The key principle is to separate understanding, transformation, and verification. Let AI accelerate the first two, but never let AI-generated explanations substitute for executable tests.
For .NET specifically, note that Microsoft's documentation now marks the standalone .NET Upgrade Assistant as deprecated in favor of the GitHub Copilot modernization chat agent in supported Visual Studio releases.
For Java migrations, OpenRewrite has documented recipes for Spring Boot, Java versions, JUnit, and Jakarta EE, making it particularly useful for repeatable framework changes.
4. Give the AI agent a migration contract
One of the most useful practices is to provide repository-level instructions and require the agent to follow them. GitHub documents repository-specific instructions for guiding coding agents and code reviews.
Here is a reusable prompt:
:::writing{variant="document" id="58321"}
Legacy Code Modernization Task
Act as a senior software modernization engineer.
Objective
Analyze the existing application and propose a safe, incremental migration to [target framework and version].
Phase 1 — Analyze
Map the architecture, modules, dependencies, entry points, and external integrations.
Identify deprecated APIs, unsupported dependencies, framework incompatibilities, and security risks.
Document important business rules and implicit behavior.
Identify breaking changes, compatibility risks, and required configuration or database changes.
Prioritize changes by risk and dependency order.
Identify missing tests before proposing high-impact refactors.
Phase 3 — Implement
Change one bounded module or concern at a time.
Preserve existing behavior unless a behavior change is explicitly approved.
Prefer framework-supported APIs and established project conventions.
Avoid unrelated refactoring and unnecessary dependency additions.
Explain any change that cannot be automated safely.
Phase 4 — Verify
Run the relevant build, unit tests, integration tests, and static analysis.
Add regression tests for changed behavior.
Report failures honestly; never claim a test passed unless it was executed successfully.
Review security, performance, configuration, and backward compatibility.
Deliverables
For each batch, provide:
Files changed and the reason for each change.
Tests added and commands executed.
Results and unresolved failures.
Risks, assumptions, and rollback instructions.
The next recommended migration step.
Do not proceed to a broad rewrite without an approved plan and a working verification strategy.
:::
5. Common failure modes to avoid
Rewriting everything at once: produces large diffs that are difficult to debug and review.
Trusting generated tests without checking them: tests can reproduce the new implementation's assumptions rather than protect existing behavior.
Migrating frameworks and architecture simultaneously: makes it difficult to determine which change caused a regression.
Ignoring hidden dependencies: reflection, dynamic configuration, stored procedures, scheduled jobs, and undocumented integrations can break even when the project compiles.
Optimizing for lines of code changed: a successful migration is measured by behavioral correctness, maintainability, reliability, and delivery risk—not code volume.
My recommendation
Start with one AI coding agent plus deterministic migration tooling plus a CI test pipeline. Add enterprise migration tooling if you have many applications, complex infrastructure dependencies, or a mainframe estate.
The best combination depends on what you are modernizing. Which describes your project most closely?
AI Coding Assistants : Tools like GitHub Copilot and Cursor excel at context-aware line and function-level refactoring, helping rewrite old syntactic patterns into modern idioms.
Specialized Agentic Workflows : Multi-file refactoring agents like Aider or Continue allow you to point an LLM at an entire directory tree to orchestrate sweeping framework migrations across multiple interdependent files.
Automated Migration & Transformation Tools : Enterprise platforms such as combine deterministic AST (Abstract Syntax Tree) recipes with AI to safely handle repetitive framework upgrades (like Spring Boot or Java versions) without breaking compiler types.
AI tools and workflows for modernizing a legacy codebase
The most effective way to modernize a legacy application is to combine AI coding agents, automated refactoring tools, architecture analysis, and test-driven migration. Rather than asking an AI to rewrite the entire application in one go, use it to understand the system, plan incremental changes, automate repetitive transformations, and verify behavior after every step.
The right toolset depends on whether you're upgrading an existing framework, moving to a different language, breaking a monolith into services, or replacing an obsolete platform.
1. The tools worth evaluating
OpenRewrite — automated refactoring
(Best for repeatable migrations)
Ideal for Java and JVM modernization, dependency upgrades, API changes, framework migrations, and bulk source transformations. Its recipes can combine many small changes into a repeatable migration. It also supports several other languages and configuration formats.
Start here for Spring, Java, dependency, and build-file migrations.
Useful for explaining unfamiliar code, generating characterization tests, proposing refactors, making changes across related files, and helping developers work through migration tasks.
Best for developer-led migrations with frequent code review.
Useful for tracing behavior across files, understanding complex control flow, proposing a migration plan, implementing a bounded change, running tests, and iterating on failures.
Best for cross-cutting changes that require understanding business logic.
One important distinction: OpenRewrite is primarily a deterministic transformation engine, whereas coding agents are better at interpreting context and handling changes that require judgment. These approaches complement each other.
For a more migration-focused option, also evaluate Microsoft's GitHub Copilot modernization tooling. Its modernization workflows cover assessment, planning, and execution, with support for Java, .NET, C++, and JavaScript/TypeScript use cases.
OpenRewrite + Amazon Q Developer or Copilot modernization
Spring Boot or Java EE upgrades
OpenRewrite + coding agent
.NET Framework to modern, cross-platform .NET
Amazon Q Developer or Copilot modernization
Legacy PHP, Python, Ruby, or Node.js application
Claude Code or Copilot + tests and static analysis
Monolith to modular monolith or microservices
OpenRewrite is particularly attractive for repeatable framework migrations, while Amazon Q has dedicated Java and .NET transformation workflows.
3. A practical end-to-end AI modernization workflow
Phase 1 — Assess and understand
Inventory languages, dependencies, entry points, database access, integrations, deployment infrastructure, and critical business workflows. Have an AI agent map module dependencies and identify unsupported libraries and architectural risks.
Deliverable: system map, risk register, and prioritized migration backlog.
Phase 2 — Establish behavioral safety nets
Generate characterization tests around existing behavior before refactoring. Add integration tests for databases, APIs, authentication, and important business rules. Capture representative production inputs and outputs where safe and appropriate.
Deliverable: baseline test suite and known-good behavior.
Phase 3 — Automate mechanical changes
Use codemods and migration recipes for API replacements, dependency updates, package renames, configuration changes, and framework upgrades. Run the transformations on a branch and inspect the diffs.
Use a coding agent to extract domain logic from UI, database, or framework-specific code; define interfaces; separate modules; and migrate one bounded capability at a time.
Prefer a modular monolith first unless independent deployment or scaling justifies microservices.
Deliverable: a modernized slice with clear boundaries and preserved behavior.
Phase 5 — Validate continuously
Require automated builds, unit and integration tests, linting, dependency and security scans, and code review for every pull request. Use the AI agent to diagnose failures, but require evidence that the fix works.
4. Make the AI agent work from a migration specification
A strong workflow uses a persistent set of instructions, an architecture decision record, and a task list rather than relying on one giant prompt.
Here is a starter prompt you can use with a repository-aware coding agent:
COPY-READY PROMPT
Act as a senior software modernization engineer.
Goal:
Migrate this legacy application from its current stack
to the target stack described below.
Rules:
1. Do not rewrite the entire application at once.
2. First inspect the repository, build system, dependencies,
tests, entry points, and deployment configuration.
3. Document the architecture and critical business workflows.
4. Identify unsupported dependencies and migration risks.
5. Propose a phased migration plan with independently
testable milestones and rollback strategies.
6. Establish characterization tests before changing behavior.
7. Automate mechanical changes where reliable tooling exists.
8. Make one bounded change at a time.
9. Run relevant tests and builds after every change.
10. Never silently change business rules, data semantics,
authentication, or public API contracts.
11. Report assumptions, failed tests, and unresolved risks.
12. Do not deploy or perform destructive operations.
Start with assessment only. Do not edit production code yet.
Rewriting everything at once. This makes regressions harder to isolate and increases the chance of losing undocumented behavior.
Trusting generated tests blindly. AI-generated tests can encode the same incorrect assumptions as the generated implementation. Review them against business requirements and observed behavior.
Mixing migrations together. Separate language upgrades, framework changes, database migrations, and architectural refactoring when possible.
Allowing unrestricted agent access. Use version control, isolated branches or worktrees, restricted credentials, and human approval for sensitive operations.
Measuring progress by lines rewritten. Track test coverage of critical paths, migration completion, defect rates, build reliability, deployment safety, and operational performance instead.
My recommendation
For most teams, start with one coding agent + automated migration tooling + a strong CI test suite. Add specialized modernization platforms only where the language, framework, or scale justifies them.
To narrow this to a concrete toolchain, tell me one thing: what is your current stack, and what do you want to migrate to?
For example, Java/Spring to modern Spring Boot, PHP to Node.js/TypeScript, .NET Framework to modern .NET, or a monolith to modular services. I can then recommend the most suitable tools and a migration plan for that specific codebase.
Refactoring and migrating a legacy codebase using AI requires combining deterministic analysis tools with generative AI agents . Because legacy systems often suffer from hidden dependencies, lack of documentation, and brittle architectures, a structured workflow prevents AI-generated bottlenecks during code review and governance.[](https://www.gartner.com/reviews/market/ai-augmented-code-modernization-tools)
Advanced LLM CLI & IDE Agents: Tools like Claude Code, , and act as interactive pair programmers that can traverse multi-file legacy repositories, execute tests, and implement incremental refactoring blocks.
Upgrade one module, service, or vertical slice at a time. Keep old and new implementations compatible where necessary, and use an adapter or strangler pattern if the whole application cannot be replaced at once.
Validate and repeat
Run builds, unit and integration tests, static analysis, security scans, and regression tests after every batch. Review diffs, measure failures, and roll back changes that don't meet acceptance criteria.
Mainframe or large enterprise portfolio
AWS Transform or comparable enterprise modernization tooling + human architecture review
OpenRewrite
LLM Chat & Analysis Interfaces : Platforms like Claude (with large context windows) are ideal for pasting legacy monolithic scripts to analyze dependency maps, design modular replacement patterns, and generate step-by-step migration plans.
If you want to narrow this down, let me know:
What legacy language/framework you are migrating from, and what modern stack you are targeting.
Whether your codebase is open-source or proprietary , which impacts which local vs. cloud tools you can use.
I can recommend a tailored workflow and toolchain for your specific project.
Coding agent + dependency and architecture analysis
Legacy database or SQL migration
Database migration tooling + AI-assisted query conversion and validation
Mainframe or COBOL modernization
Specialized enterprise modernization tooling, such as AWS's mainframe modernization capabilities
GitHub Copilot Workspace
Cursor
AST and Static Analysis Companions: Combining LLMs with traditional AST (Abstract Syntax Tree) parsers ensures that structural renames, language syntax conversions (e.g., Python 2 to 3, or Java 8 to 21), and API rewrites adhere strictly to compilation rules.
Safety Net (Test Generation): Before touching legacy logic, prompt your AI agent to write comprehensive unit and integration tests (using coverage tools) for the existing codebase to lock down current behavior.
Incremental Strangler Fig Refactoring: Isolate small, loosely coupled domains or endpoints. Feed the legacy module and target modern framework requirements into your AI tool to rewrite it piece by piece rather than attempting a high-risk monolithic rewrite.
Automated Translation with Validation: For direct language or framework upgrades, let AI agents handle the syntax translation, immediately followed by running your test suite to catch runtime regressions or subtle type mismatches.
Human-in-the-Loop Governance: Because AI code generation can introduce subtle logical flaws or security anti-patterns in unfamiliar legacy structures, enforce strict pull request reviews focused specifically on edge cases, state management, and memory safety.
If you'd like to narrow this down, please tell me:
What source language/framework and target stack you are migrating from and to
The approximate size (lines of code) of the legacy codebase
Whether you have existing automated tests
I can tailor a precise toolchain and workflow for your migration.
![[MM’s] Boot Notes — OpenRewrite. Automated, auditable migrations to Java… | by Matej Marconak | Jan, 2026 | Medium](https://images.openai.com/static-rsc-4/c-ggT5UO0csk7LN32TQ8NfQ4RxPW3EOz-ncwuhy4e5deR14xaqEtGdJcGU0VCIpZ80s--6eo0hUXOl3GdnxsVo0Gyq2A6uodfkl-OW2E8mkYsjvdE8HC9acW4jlql_H7jjI1UoPK9jVk0jRxaTBn7hhc4nJ5Aw2uC4aDWx54p_XPt197AREThZEXOtcR-M13?purpose=fullsize)
