Internship

AI-Assisted Legacy Code Migration Research

Investigating how AI tools and LLM's can support developers when migrating legacy applications to modern technology stacks.

Information

Role

Front-end developer

Duration

3 months

Company

Sweet Mustard

Technologies

React, Java, Ruby on rails

Year

2026

Introduction

During my internship, I researched how modern AI tools can assist developers in migrating legacy software systems to newer technology stacks. The goal was to evaluate and validate the effectiveness, limitations, and practical applications of AI throughout the migration process.

Challenges

illustration challenge
  • Use of outdated technologies.
  • Understanding complex legacy code with limited documentation.
  • Maintaining functional equivalence during migration.
  • Verifying the correctness of AI-generated implementations.

Goals

illustration challenge
  • Analyze current AI tools for software migration.
  • Identify migration tasks where AI provides value.
  • Evaluate strengths and weaknesses of AI-generated solutions.
  • Develop guidelines for developers using AI during migrations.

Research approach

During the internship, we evaluated a variety of AI-assisted software modernization approaches. Rather than relying on a single tool or workflow, we experimented with different techniques to understand their strengths, limitations, and suitability for legacy code migration. We used a real legacy codebase as experimental subject rather than creating simplified examples.

Technologies & tools

Legacy Technologies
Modern Stack
AI

Our workflow typically involved:

01

Selecting a legacy feature or component.

Illustration selecting

02

Defining a target modern technology stack.

Illustration defining

03

Using AI tools and LLM's to migrate legacy code.

Illustration generating

04

Comparing different prompting and migration strategies.

Illustration comparing

05

Reviewing, testing, and refining the generated code.

Illustration reviewing

06

Measuring maintainability, correctness, and development effort.

Illustration measuring

Results & conclusion

The research showed that AI can significantly accelerate migration-related tasks, particularly in code understanding, documentation generation, and initial code transformations. However, developer oversight remains essential for validating architectural decisions, ensuring correctness, and handling domain-specific business logic.

Personal reflection

This internship gave me insight into the challenges organizations face when modernizing software systems. I learned how AI can act as a powerful assistant rather than a replacement for developers, and I gained hands-on experience evaluating emerging AI tools in real-world software engineering scenarios.

Technical skills

  • Software Migration
  • Legacy System Analysis
  • AI-Assisted Development
  • Prompt Engineering
  • Software Architecture
  • Code Refactoring

Research skills

  • Functional analysis
  • Experiment design
  • Comparative Evaluation
  • Documentation

Key takeaways

Key takeaways
AI Accelerates Migration

Speeds up repetitive migration tasks.

Key takeaways
Human Expertise Remains Essential

Critical decisions still require developers.

Key takeaways
Documentation Improves

AI excels at explaining and documenting legacy code.

Key takeaways
Best Used as a Copilot

Most effective when combined with developer review.