AI for Legacy Code Modernization: Strategies That Actually Work

The Legacy Code Problem

Most teams spend more time maintaining existing systems than building new ones. Legacy codebases -- old languages, no tests, outdated patterns, departed developers -- represent significant risk. AI changes the economics of modernization.

Strategy 1: Generate Tests First

def generate_tests(code: str, language: str) -> str:
    return client.messages.create(
        model='claude-opus-4-6', max_tokens=4096,
        system=f'Generate comprehensive {language} tests. Include edge cases, error conditions, and boundary values.',
        messages=[{'role': 'user', 'content': code}]
    ).content[0].text

Run against current code. Failing tests reveal wrong assumptions. Passing tests establish your refactoring safety net.

Strategy 2: Documentation Recovery

Claude generates function docstrings, module overviews, and architecture documentation from import graph analysis -- documenting behavior as it actually exists.

Strategy 3: Incremental Migration

1. Identify a leaf function with no legacy dependencies
2. Claude translates to target language preserving exact behavior
3. Run tests against both original and translation
4. Replace when tests pass. Repeat.

Common Pitfalls

Big-bang rewrites with AI fail for the same reasons they fail without AI. Incremental, test-driven modernization succeeds. Always have a domain expert review AI output -- domain logic is subtle and Claude may generate syntactically correct but semantically wrong code.