Updated June 2026

AI for Legacy Code Modernization

Class Duration

14 hours of live training delivered over 2-3 days to accommodate your scheduling needs.

Student Prerequisites

Professional software development experience, ideally with exposure to large or aging codebases
Familiarity with at least one AI coding assistant

Target Audience

Senior software engineers, architects, and engineering managers responsible for modernizing legacy applications. Particularly relevant for teams facing large-scale migrations (language upgrades, framework replacements, cloud migrations) who want to use AI agents to accelerate the process without sacrificing reliability. Because a test safety net is the foundation of safe modernization, this pairs naturally with AI-Driven Test Generation and Maintenance.

Description

For multi-week team training on this material, see the AI for Legacy Modernization Academy. Legacy codebases are where AI assistance can add disproportionate value - and where the risks of blind trust in AI output are highest. This course covers the AI-assisted modernization lifecycle: using agents to read, map, and document legacy code; building a test safety net before making changes; applying incremental migration patterns (strangler fig, anti-corruption layer) with AI assistance; agent-driven refactoring and language/framework upgrades; and validating the results. Participants work with realistic legacy code scenarios throughout the labs.

Learning Outcomes

Use AI agents to generate documentation, dependency maps, and architectural summaries for unfamiliar legacy code.
Build a test safety net for untested legacy modules using AI-assisted test generation.
Apply the strangler fig pattern with AI assistance for incremental component-by-component migration.
Drive AI-assisted refactoring within defined boundaries to reduce technical debt progressively.
Perform language and framework upgrade tasks (e.g., Python 2→3, Java 11→21, jQuery→React) with agent support.
Validate AI-assisted changes against generated test suites and behavioral contracts.
Design a phased modernization roadmap that incorporates AI tooling at appropriate stages.

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

A modern IDE with AI coding assistant, language runtime matching the lab (Python or Java), and Git.

Training Topics

Reading Legacy Code with AI

Generating module and function summaries
Dependency graph extraction
Identifying coupling and hidden assumptions
Documenting behavior before changing it

Building a Test Safety Net

AI-assisted test generation for untested modules
Characterization tests: capturing existing behavior
Mutation testing to validate test adequacy
Coverage targets for legacy migration work

Incremental Migration Patterns

Strangler fig pattern with AI assistance
Anti-corruption layer design
Branch-by-abstraction and feature flags
Prioritizing components for early migration

Agent-Driven Refactoring

Safe refactoring scope definition for AI agents
Automated rename, extract, and inline refactors
Reducing cyclomatic complexity with agent assistance
Review workflow for AI-driven refactoring diffs

Language and Framework Upgrades

AI-assisted syntax and API migration
Dependency upgrade and compatibility resolution
Framework-specific migration approaches (Python, Java, JavaScript)
Validating behavior equivalence after migration

Architecture Documentation Generation

Generating C4 model components from code
API and data model documentation
Architecture decision record (ADR) drafting with AI
Keeping documentation synchronized with code changes

Modernization Roadmap Design

Assessing modernization ROI with AI tooling
Sequencing: documentation → tests → strangler → full migration
Risk management in AI-assisted migration projects
Communicating progress to stakeholders