Reverse Engineering Legacy Codebases with AI: From Mystery to Map

The Legacy Code Problem

Every engineering team inherits one eventually: a sprawling, undocumented codebase built by people who’ve long since moved on. No architecture diagrams, no README worth the name, just hundreds of thousands of lines of code in a language you may not even use for new projects.

Traditionally, reverse engineering this kind of system is slow, manual, and risky:

• Weeks of reading code just to understand data flows.
• Trial-and-error debugging to figure out what a function actually does.
• High risk of breaking critical paths because you don’t know what depends on what.

Now, AI can help close that knowledge gap in hours instead of weeks — by processing code in chunks, generating functional descriptions, call graphs, and even modernization suggestions.

From Code Dump to Working Map

AI doesn’t “replace” human code comprehension, but it does change the shape of the work. Instead of line-by-line spelunking, you can:

• Feed AI portions of the codebase (file by file, module by module).
• Get back clear explanations of each component’s role.
• See visual call graphs to understand how modules talk to each other.
• Receive flagged areas for possible refactor, deprecation, or security review.

This turns reverse engineering into a guided discovery process, not a blind expedition.

The Chunking Approach

Large language models (LLMs) have context limits — you can’t just drop your entire 500K-line repo into one prompt. The key is chunking:

1. Static Analysis First — Use tools like ctags, tree-sitter, or clang to extract function/method definitions, class hierarchies, and dependencies.
2. Module-Level Chunks — Feed AI one logical module at a time, including related files for context.
3. Cross-Reference Linking — Store the AI’s outputs in a searchable index so related modules can be connected.
4. Iterative Refinement — After all modules are processed, prompt the AI with summaries and dependency maps to create a system-level view.

What You Can Get Out of It

Functional Descriptions

For each function or class, AI can:

• Describe its purpose and inputs/outputs.
• Summarize algorithm logic in plain English.
• Highlight possible external dependencies (e.g., API calls, database queries).

Call Graphs

Generate diagrams showing:

• Which functions call which.
• Cross-module dependencies.
• Orphaned functions or dead code.

Upgrade & Refactor Recommendations

Based on known patterns and best practices, AI can:

• Identify outdated libraries or language features.
• Suggest migration paths (e.g., Python 2 → 3, AngularJS → React).
• Flag high-complexity functions for refactoring.

Security & Compliance Flags

By combining code analysis with security rule sets, AI can:

• Spot hard-coded credentials.
• Identify unsanitized inputs.
• Flag unencrypted data handling.

Guardrails for Accuracy

AI output is powerful but not infallible. To make it reliable in production work:

• Validate with Static Analyzers — Cross-check AI descriptions against results from SonarQube, Semgrep, or ESLint.
• Keep a Human in the Loop — Treat AI analysis as a first draft, not a final verdict.
• Version Outputs — Store generated docs in the repo so they evolve with the code.
• Restrict Scope — In regulated environments, run AI inside a private, compliant environment (AWS Bedrock, Azure OpenAI).

Proof in Practice

MediaNet — PHP Monolith Migration
We used an AI-assisted pipeline to process 300K lines of PHP, generating module summaries and call graphs. What had been a 6-week manual audit collapsed to 9 days, with a 40% reduction in migration planning errors.

Atigeo — NLP Service Inventory
By chunking and analyzing legacy Java services, we mapped undocumented endpoints, removed 17% dead code, and accelerated an API modernization project by 30%.

T-Mobile — Compliance Audit Prep
Analyzing legacy automation scripts with AI surfaced 12 hard-coded secrets and several insecure API calls in days, not weeks.

Workflow Blueprint

1. Code Inventory
   • Clone repo, identify languages/frameworks, run dependency scans.
2. Pre-Processing
   • Extract symbol tables, function signatures, and module trees.
3. Chunking
   • Divide into logical units, preserving internal references.
4. AI Analysis
   • Prompt model for functional description, dependencies, and risks per chunk.
5. Aggregation
   • Merge outputs into a searchable knowledge base.
6. Visualization
   • Generate call graphs and dependency diagrams.
7. Review & Validate
   • Human review plus static analysis cross-check.

Risks and How to Mitigate

• Hallucinated Behavior
  • Solution: Cross-check with unit tests or controlled code execution.
• Security Exposure
  • Solution: Never send proprietary code to public APIs; use private hosting with encryption.
• Misleading Upgrade Advice
  • Solution: Have architects review recommendations against business requirements.

Why This Matters for Velocity

Reverse engineering is often the longest lead-time item in modernization projects. Compressing that timeline means:

• You can plan migrations faster.
• You can de-risk changes earlier.
• You free senior engineers from spending weeks in code archaeology.

Just as CI/CD made deployments routine, AI-assisted codebase mapping can make legacy modernization predictable.

The Vectorworx.ai Position

Legacy systems aren’t going away — but the time we spend understanding them can. AI lets us build a living, navigable map of what’s there, where it’s safe to change, and where the biggest risks hide. The point isn’t to replace engineers; it’s to get them from “What is this?” to “Here’s what we do next” in days, not months.

If you’re sitting on a black-box codebase, the smartest move is to start mapping it now — before you have to make changes under pressure.

Need to scale operations under pressure? Contact Vectorworx.ai to deploy automation that stands up to real-world extremes.

References

• SonarQube Documentation

  — Static analysis and code quality platform for identifying bugs, vulnerabilities, and code smells.

• Semgrep Documentation

  — Lightweight static analysis for security, correctness, and maintainability.

• AWS Bedrock

  — Private hosting for foundation models with governance and compliance controls.

• Tree-sitter

  — Incremental parsing system for building code analysis and understanding tools.

• JetBrains — Code Structure Analysis

  — IDE-integrated tools for exploring call hierarchies and dependencies.