Chapter 13: Legacy System Migration Strategy

1 Challenges of Legacy Systems

Legacy systems are problems faced by almost all mature enterprises:

  • Massive code: Tens of thousands or even hundreds of thousands of lines of code, with complex and intricate logic
  • Missing documentation: Original architecture documents are long outdated or never existed
  • Knowledge loss: Original developers have left, tacit knowledge is lost
  • Risk sensitivity: Systems carry core business, afraid to make changes lightly

Under the Agent-oriented paradigm, legacy system challenges are even more prominent:

  • AI struggles to understand code without “business requirements”
  • Lack of constraint networks, AI easily introduces risks during modifications
  • Context overflow (files too large, exceeding AI context limits)

2 Core Migration Thinking

Not Rewrite, But “Reverse Engineering Business Requirements”

We don’t rewrite legacy systems, but through the following steps, make legacy systems “business-requirement-oriented”:

Legacy code
    ↓
AI-assisted analysis (layered summarization, dependency analysis)
    ↓
Human expert confirmation and supplementation
    ↓
Generate structured business requirement documents
    ↓
Establish constraint networks
    ↓
Legacy system becomes "business-requirement-driven" system

Core Principles

  • Incremental: Migrate one module at a time, rather than rewriting everything at once
  • Conservative: Prioritize migrating low-risk, frequently changing modules
  • Verifiable: Every migration step has verification mechanisms to ensure no damage to existing functionality
  • Rollback-capable: Any change can be rolled back to reduce risk

3 Phased Migration Plan

Phase 1: Exploration and Understanding (1-3 months)

Goal: Establish global cognition of legacy systems

Key activities:

  1. Code panoramic scanning

    • Use tools to generate code statistics (line count, file count, module count)
    • Generate dependency relationship diagrams
    • Identify core modules and boundary modules

  2. AI-assisted code analysis

    • For each core module, use AI to generate summaries
    • Use “layered summarization” strategy for oversized files
    • Generate inter-module call relationship diagrams

  3. Expert knowledge extraction

    • Interview veteran employees familiar with the system
    • Record business rules and tacit knowledge
    • Mark historical decisions and context

  4. Output requirement assets

    • System architecture technical requirement documents
    • Core module business requirement documents
    • Key business rule lists

Phase 2: Establish Constraints and Pilot (3-6 months)

Goal: Establish constraint networks, validate business-requirement-driven model

Key activities:

  1. Architecture constraint extraction

    • Extract architecture rules from existing code
    • Transform into machine-readable constraints
    • Establish CI checks

  2. Select pilot modules

    • Choose low-risk, frequently changing modules
    • Based on knowledge extracted in Phase 1, write structured business requirement documents

  3. Pilot business-requirement-driven development

    • For new requirements in pilot modules, use business-requirement-driven model
    • Validate whether “requirement assets” are clear enough
    • Adjust and improve business requirement documents

  4. Output results

    • Reusable “module business-requirement transformation” methodology
    • Team initially masters business requirement engineering practice

Phase 3: Business-Requirement-Driven Incremental Evolution (6-18 months)

Goal: Promote business-requirement-oriented practice to more modules

Key activities:

  1. Establish internal “Requirement Engineering Platform”

    • Requirement management: Store, version, and retrieve all module business requirement documents
    • Constraint library: Maintain enterprise-level architecture rules, security baselines
    • AI assistant integration: Automatically display module business requirement summaries in development environments

  2. Incorporate “business-requirement-oriented” into development process

    • New feature development: Must first write business requirements
    • Legacy module modifications: Must first consult or update business requirement documents
    • Code review upgrade: Review “whether code follows module business requirements”

  3. Continuously clean up technical debt

    • Regularly run AI analysis to identify “business-requirement deviation” code
    • Generate debt reports, incorporate into iteration fixes

  4. Output results

    • 80%+ of core system modules complete business requirement transformation
    • New feature development efficiency improved by 50%+
    • Legacy systems become maintainable and evolvable

Phase 4: Fully Business-Requirement-Driven (18+ months)

Goal: Legacy systems become “requirement asset pools” understandable by AI

  • All historical modules have clear business requirement documents and constraint rules
  • New requirements can directly generate implementation plans by AI based on requirement assets
  • Human engineers mainly focus on strategic business requirement definition and system innovation

4 Practical Techniques for Handling “Context Overflow”

Layered Summarization Strategy

When oversized files must be analyzed:

  1. Split files into fragments by function/class
  2. Let AI generate summaries for each fragment (within 200 words)
  3. Concatenate all summaries, let AI analyze overall structure
  4. Based on overall understanding, then dive into key fragments

Dependency Graph Guidance

  • Use code analysis tools to generate dependency graphs
  • When needing to understand a function, first let AI analyze “direct callers/callees”
  • Avoid loading all indirect dependencies at once

Semantic Chunking

  • Automatically split based on code semantics (class definitions, function comments)
  • Use embedding models to vectorize code fragments
  • When needing a certain function, first retrieve most relevant fragments

Incremental Refactoring

  • Don’t try to refactor the entire system at once
  • Change one small module each time, simultaneously update its business requirement document
  • Over time, the system’s overall business-requirement-oriented degree naturally improves

5 Organizational-Level Guarantees

Special Task Force

Establish “Legacy System Business Requirement Transformation” special task force:

  • Architects: Responsible for overall strategy and technical gatekeeping
  • Senior developers: Familiar with code, responsible for specific implementation
  • Product managers: Provide business perspective, ensure business requirement accuracy

Incentive Mechanisms

  • Veteran employee incentives: Their experience is valuable assets, encourage them to share knowledge through bonuses, honors, promotion channels
  • Team incentives: Incorporate business requirement transformation progress into team goals, give rewards upon achievement

Training System

  • Organization-wide business requirement thinking training
  • One-on-one knowledge extraction for veteran employees
  • Regular business requirement transformation best practice sharing

6 Chapter Summary

Legacy system business requirement transformation:

Core thinking:

  • Not rewrite, but “reverse engineering business requirements”
  • Incremental, conservative, verifiable, rollback-capable

Four phases:

  1. Exploration and understanding: Establish global cognition
  2. Establish constraints and pilot: Validate model
  3. Business-requirement-driven incremental evolution: Comprehensive promotion
  4. Fully business-requirement-driven: Legacy systems become assets

Key cognition:

  • Legacy systems are not garbage, but “dark knowledge mines”
  • Business requirement transformation is the process of mining, refining, and structuring dark knowledge
  • Returns are exponential: knowledge no longer lost, newcomers onboard faster, architecture controllable, business agile

When systems complete business requirement transformation, as GitHub spec-kit demonstrates:

When specifications become core assets, code is just their expression