Top 9 AI Tools for Legacy Application Modernization

Most legacy modernization projects run over time and over budget. Development teams spend the first month reading old code. But in almost all cases, documentation is wrong or missing. Nobody is sure what touches what before anything changes.

AI tools don’t solve that. They can’t determine whether to use the Strangler Fig pattern or a full rewrite, or which services to extract first. What they do is cut the cost of the mechanical work: reading unfamiliar codebases, generating test suites, and scaffolding new structures.

Across this article, we’ll discover tools that are concretely useful in migration work. Some are general coding assistants. Others are purpose-built for modernization. Each entry notes what the tool does well and where it falls short.

What “Legacy Modernization” Actually Covers

Legacy migration work usually breaks into several categories:

• Codebase comprehension: this one is all about reading code you didn’t write, often without documentation.
• Dependency analysis, whose main purpose is to identify what touches what before anything changes.
• Refactoring and decomposition: extracting services, updating APIs, splitting monoliths
• Language translation: which involves all the steps needed to move from one stack to another (COBOL to Java, Java EE to Spring Boot, .NET Framework to .NET)
• Test generation: which includes unit and integration tests for untested legacy code
• Documentation: generating inline documentation, architecture summaries, and migration notes.
• API wrapping: exposing legacy functionality to modern systems without rewriting the core

There are different tools that cover different parts of this. Let’s go through each one of them.

Claude Code (Anthropic)

Claude Code is one of the most popular command-line AI coding agents. It operates across an entire repository, not just the file you have open. For legacy codebases, that’s the most practically useful property any tool can have. A native VS Code extension launched in 2025, so terminal-only operation is no longer the only path. The CLI remains the better option for large-scale analysis, though.

Point it at a full Java EE repo. Then write a prompt like the one below to ask it to summarize a legacy module and flag external dependencies:

“Summarize what the com example.billing package does.

List all external service calls, database access patterns,

and any obvious technical debt. Format as a technical brief.”

Claude Code can explain what a module does and can also tell you the dependency relationships. It also exposes technical debts or things that can be refactored to improve quality.

Chudovo’s team has found Claude Code useful when onboarding into inherited codebases. Instead of two weeks of reading code, engineers ask Claude Code for a plain-language summary:

• What this module does
• What it depends on
• Where the risk surface is.

The output is rarely perfect, but it’s accurate enough to reduce onboarding time and surface the things worth verifying manually.

• Strengths: Large context window, excellent test generation, strong legacy language comprehension, including COBOL and Java, works across Python, .NET.
• Limitations: CLI-first workflow requires setup. Slow on very large analysis requests. API-based pricing adds up on long sessions.

GitHub Copilot (Microsoft)

Copilot is among the most widely used AI coding tools in production teams. Its main value is in its strong IDE integration. It works well with the most popular editors such as VS Code, Visual Studio, and IntelliJ, generating completions and suggestions as you write.

In a migration context, Copilot is most useful once the approach is set and an engineer is actively rewriting code. It does a great job following patterns you establish. Once you refactor one service correctly, Copilot applies the same approach to the next file pretty fast. Across dozens of similar classes in a migration, the time savings are real.

Microsoft added a structured modernization workflow: Copilot generates an assessment and produces a plan.md that includes upgrade strategies and risk mitigations, and breaks that plan into sequential tasks. For .NET Framework-to-.NET and Java upgrade scenarios, this workflow is worth evaluating specifically.

• Strengths: Best IDE integration, fast, follows established patterns, broad language support, widely familiar to most teams.
• Limitations: Limited whole-codebase comprehension, needs clear context to produce accurate output, tightly coupled to the Microsoft/OpenAI ecosystem.

Cursor

Cursor is a VS Code fork with AI built into the editor at a deeper level than any extension. Two features matter most for legacy work.

• Plan Mode: before executing any changes, Cursor drafts a step-by-step plan. In a migration context, this matters. Instead of generating code and hoping it holds together, Cursor presents a structured proposal for a multi-file refactor. You review and edit the plan before a single file change.
• Codebase indexing: Cursor runs indexing over the entire project and understands the relation between modules when suggesting changes. This is important in large legacy repos where a change in one package can cascade unexpectedly.

Chudovo’s team has used this on Java and Python projects with no tests and minimal documentation. Describe the decomposition task, let Cursor produce the plan, review it, then execute. Cursor generates test files alongside refactored code, and flags functions it couldn’t test confidently.

• Strengths: Plan Mode for controlled multi-file changes, strong project indexing, model-agnostic (pairs well with Claude), dedicated legacy refactoring workshop materials.
• Limitations: File-centric by design. Very large multi-module systems need deliberate context setup. Less suited for full codebase analysis than Claude Code.

Amazon Q Developer (AWS)

Amazon Q Developer has a standout feature for Java-heavy legacy work: automated Java version upgrades. It takes a Java 8 or 11 application and upgrades it to Java 17 or 21. It patches dependencies, updates deprecated API calls, and flags what it couldn’t resolve automatically.

The CLI version (generally available June 2025) added Oracle-to-PostgreSQL SQL migration as part of the same workflow. In many upgrade scenarios, teams report reducing weeks of manual assessment and remediation work to a significantly shorter review-and-validation cycle. Teams still review the output, but the starting point is far ahead of where a manual approach would begin.

For teams already inside AWS, Q also understands AWS SDK patterns, CDK syntax, and CloudFormation, reducing errors when infrastructure code needs updating alongside application code.

• Strengths: Java version upgrade automation (8/11 to 17/21). Good while working with AWS. Oracle to PostgreSQL migration added in 2025. Generous free tier.
• Limitations: Translation features are Java and .NET-focused. Less useful for COBOL, RPG, or other legacy languages. General coding assistance is solid but not the best on this list.

IBM watsonx Code Assistant for Z (+ OpenLegacy)

Watsonx Code Assistant for Z is built for COBOL and PL/I on IBM Z mainframe systems. It supports key modernization activities, including application analysis, service extraction, and selective COBOL-to-Java translation.

The approach is incremental. IBM identifies a business service in COBOL, extracts it, translates it to Java, validates it, and then runs old and new code in parallel during testing. The underlying model is fine-tuned specifically for COBOL-to-Java translation, which produces better results than a general-purpose LLM on the same task.

The 2026 release added agentic capabilities: automatic dependency identification, impact analysis, code generation, rule enforcement, and compilation verification in a single run.

Not every mainframe team is ready for code translation, though. Some need to expose legacy functionality to modern systems first, without touching production code at all. That’s where OpenLegacy fits. It auto-generates cloud-native APIs directly from mainframes, IBM i (AS/400), CICS, and VSAM systems, with no changes to the underlying source. Financial institutions and insurance companies use it specifically because the risk surface stays low. It’s a bridge, not a transformation path — but on systems that process millions of daily transactions, that distinction matters.

For mainframe organizations facing a skills gap as COBOL developers retire, Watsonx Code Assistant for Z is the most credible option for full modernization. OpenLegacy is the right call when the immediate priority is integration, not rewriting.

• Strengths: Purpose-built COBOL/PL/I to Java translation. Incremental, validated approach. Agentic dependency analysis (2026). Low-risk API integration from OpenLegacy.
• Limitations: IBM Z and IBM i mainframes only. Both tools require existing IBM infrastructure. Not relevant outside the mainframe context. Generated Java code may require significant cleanup to follow modern patterns. IBM is transitioning this product line to IBM Bob, its next-generation mainframe AI platform.

Kodesage

Kodesage is a knowledge extraction platform. It ingests a legacy codebase alongside issue tickets, database schemas, wikis, and documentation, then builds a searchable knowledge graph of what the system actually does.

Many projects stall not because teams can’t write modern code, but because they don’t understand the existing system well enough to change it safely. A COBOL program accreted over 30 years, with logic split across stored procedures and three schema versions, doesn’t yield to a quick read. Kodesage is built for that problem.

It supports COBOL, PL/SQL, PowerBuilder, Oracle Forms, SAP, and RPG. On-premise and VPC deployment options make it viable for regulated industries where source code can’t leave the network.

• Strengths: Great tool built for legacy knowledge extraction. Strong on COBOL, PL/SQL, and PowerBuilder. On-premise deployment. It generates dependency graphs, documentation, and test scaffolding.
• Limitations: Not a transformation or code-generation tool. Newer platform with a limited published track record. Pricing not publicly listed.

Rhino.ai

Rhino.ai is an end-to-end modernization platform covering discovery, design, and transformation. It extracts business rules from legacy code, SaaS apps, workflows, and documentation, organizes them into a governed logic graph, and generates modern code in the target architecture.

The Universal Application Notation (UAN) framework separates the discovery step from the output step. Extracted business logic is represented in a platform-agnostic format first. The same rules can then target microservices, low-code platforms (ServiceNow, Appian), AWS, GCP, or on-premise deployments. This matters for organizations that don’t want to commit to a single target architecture before they understand what they’re migrating.

For systems where critical business rules live in stored procedures and configuration files rather than in application code, Rhino.ai is one of the few tools that claims to operate on that layer directly.

The platform is available on the AWS Marketplace.

• Strengths: End-to-end discovery to deployment. Vendor-agnostic UAN framework. Handles non-code artifacts. AWS Marketplace is listed.
• Limitations: Newer platform; enterprise-scale track record still being established. Enterprise-scoped engagement model.

OpenRewrite/Moderne

OpenRewrite started at Netflix as an internal tool to remove a deprecated logging library from a massive codebase (deprecated for six years, still everywhere). It was open-sourced and is now maintained by Moderne. OpenRewrite has become one of the most commonly referenced tools for large-scale Java modernization projects.

The mechanism differs from AI-based tools. OpenRewrite operates on a Lossless Semantic Tree (LST): a precise structural representation that preserves formatting, type information, and source positions. Transformations called “recipes” are deterministic. Run the same recipe on the same code, and you get the same result every time. Over 5,000 recipes are available, covering Java version upgrades, Spring Boot 2-to-3 migrations, javax-to-jakarta namespace changes, security fixes, and more.

Moderne is the enterprise platform on top. Where OpenRewrite handles one repository at a time, Moderne runs recipes across thousands simultaneously. Keeping a security library version current across 500 Java repositories goes from a multi-team project to a scheduled job.

• Strengths: Deterministic, testable transformations. 5,000+ community recipes. Widely adopted for Java migrations. Moderne scales to thousands of repos.
• Limitations: Java-primary. Not useful for COBOL, RPG, or non-JVM stacks. Custom migration patterns require recipe authoring.

CAST Imaging

CAST Imaging is an architecture analysis tool. It produces a detailed, interactive map of a legacy codebase: components, dependencies, call chains, database access patterns, dead code, and structural violations.

The use case is the start of a migration project, before any code changes happen. Teams use it to answer questions the code alone can’t answer: Which components are tightly coupled? Where is the highest risk surface? How much dead code exists that can be eliminated before the refactoring scope is set?

• Strengths: Best-in-class support for mapping of dependencies and architecture for a wide array of enterprise technologies, including Java, .NET, COBOL, databases, and middleware components. AI context can be delivered downstream coding tools. Portfolio analysis with CAST Highlight.
• Limitations: Analysis only. No code generation, no migration execution. Requires enterprise licensing. Most useful to architects and tech leads, not individual developers.

Picking the Right Tool

Each tool on this list solves a specific problem in migration work. None of them replaces the migration plan itself. Most projects need two or three, chosen by phase and stack.

• Java/JVM modernization (version upgrades, Spring Boot migration): OpenRewrite for automated refactoring. Amazon Q Developer for Java version upgrades.
• Mainframe (COBOL/PL/I on IBM Z): The preferred choice is watsonx Code Assistant for Z. If you want to expose APIs without changing the source code, OpenLegacy is the preferred choice.
• Greenfield rewrite of a poorly documented system: CAST Imaging first for architecture visibility. Kodesage for knowledge extraction. Claude Code or Cursor for the rewrite.
• Large enterprise modernization across multiple systems: Rhino.ai for the broadest scope of discovery and transformation. CAST Imaging for portfolio visibility.
• Claude Code and Cursor are useful across all of these scenarios for comprehension, test generation, and day-to-day refactoring work.

The practical path: identify what phase the project is in, match tools to that phase, and run a two-sprint evaluation before committing. On a multi-month modernization effort, a well-matched tool versus the wrong one shows up noticeably in the timeline.