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# Leveraging Large Language Models for Legacy Code Translation: Challenges and Solutions

Translating legacy MATLAB to Python demands more than basic LLM prompts. Discover how Zeiss engineers conquered logic hallucinations using automated pipelines and cross-language debugging loops.

- **Speakers:** [Michael Niebisch](https://www.wearedevelopers.com/@michael-niebisch)
- **Event:** World Congress 2024
- **Published:** August 22, 2024
- **Duration:** 26:38
- **URL:** https://www.wearedevelopers.com/videos/1157-leveraging-large-language-models-for-legacy-code-translation-challenges-and-solutions

## Summary

To overcome the difficulty of maintaining legacy MATLAB codebases and the scarcity of specialized developers, an engineering team at Zeiss launched an initiative to translate legacy research modules into Python using large language models. The project progressed from a semi-automated chat workflow to a fully automated translation pipeline. While LLMs successfully generated boilerplate architectures and basic test structures, specific language idiosyncrasies—such as MATLAB's 1-based indexing and column-major memory against Python's 0-based indexing and row-major layout—introduced subtle logic hallucinations that required careful developer intervention.

The team discovered that feeding massive, complex files resulted in poor translation output, whereas a "divide and conquer" refactoring strategy with smaller, isolated snippets significantly improved translation accuracy. To reduce manual copy-paste fatigue, they developed an automated pipeline that enriched the prompt with statically extracted data types and shape annotations as inline comments. Because generative outputs are inherently non-deterministic, they built an empirical evaluation framework relying heavily on automated unit tests that isolated known failure patterns, enabling them to confidently quantify the success rates of various prompting techniques.

When autonomous agent auto-fixing failed, the team innovated a unique cross-language debugging loop. They injected logging instructions into both the original MATLAB and generated Python code to capture variable states at multiple execution nodes, then fed the diverging log arrays back to the model to pinpoint exactly where the program logic fractured. Finally, to address intellectual property security constraints preventing proprietary source code exposure to public APIs, the team evaluated open-source instances like Llama 2, observing that while local hosting ensures data privacy, smaller models trail state-of-the-art commercial equivalents in complex legacy refactoring capabilities.

**Keywords:** legacy code translation, matlab to python migration, large language model hallucinations, divide and conquer refactoring, automated code translation pipelines, type annotation extraction, autonomous coding agents, cross-language debugging, automated log analysis, code generation evaluation frameworks, non-deterministic output verification, unit test generation, prompt engineering metrics, intellectual property constraints, local deployment architectures, llama 2

## Chapters

1. **Introduction to semiconductor technology and algorithms at Zeiss** (00:03) — Chip manufacturing processes rely heavily on customized software for rapid defect inspection and image registration.
1. **Motivations for transitioning legacy MATLAB repositories to Python** (03:21) — Migrating legacy codebase modules highlights the need to explore large language models as automated translation tools.
1. **Semi-automatic translation workflow via standard chat interfaces** (06:02) — Manually prompting a model for translation reduces boilerplate code but can introduce subtle logic errors.
1. **Navigating array indexing and structural differences between languages** (09:29) — Language-specific properties like one-based indexing and unique memory layouts cause translation failures that necessitate a divide-and-conquer strategy.
1. **Building an automated translation pipeline and auto-fix agent** (13:53) — Deterministic workflows enable models to inject type annotations and attempt automatic bug remediation during the translation process.
1. **Evaluating model accuracy with specialized unit testing frameworks** (19:03) — Creating isolated tests for specific syntax deviations helps quantify the success rate of various structural prompting techniques.
1. **Enhancing manual debugging through model-assisted log analysis** (21:11) — Analyzing variable state logs alongside a language model helps developers pinpoint exact execution divergences between different runtimes.
1. **Addressing IP security requirements with local model deployments** (23:23) — Deploying open-source local models mitigates the risk of exposing sensitive proprietary codebase secrets to external APIs.
1. **Summary of translation reliability and audience question session** (24:31) — Automated code translation faces limitations that demand robust engineering pipelines and precise cross-language prompt instructions.

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- [Migrating legacy source code to testable modern frameworks](https://www.wearedevelopers.com/videos/100215-smart-test-automation-the-experience-of-legacy-transformation) (from "SMART Test Automation - the experience of legacy transformation")

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