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World Congress 2024 • Aug 22, 2024 • Session details

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

Michael Niebisch

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.

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#1 about 4 min

Introduction to semiconductor technology and algorithms at Zeiss

Chip manufacturing processes rely heavily on customized software for rapid defect inspection and image registration.

#2 about 3 min

Motivations for transitioning legacy MATLAB repositories to Python

Migrating legacy codebase modules highlights the need to explore large language models as automated translation tools.

#3 about 4 min

Semi-automatic translation workflow via standard chat interfaces

Manually prompting a model for translation reduces boilerplate code but can introduce subtle logic errors.

#4 about 5 min

Navigating array indexing and structural differences between languages

Language-specific properties like one-based indexing and unique memory layouts cause translation failures that necessitate a divide-and-conquer strategy.

#5 about 6 min

Building an automated translation pipeline and auto-fix agent

Deterministic workflows enable models to inject type annotations and attempt automatic bug remediation during the translation process.

#6 about 3 min

Evaluating model accuracy with specialized unit testing frameworks

Creating isolated tests for specific syntax deviations helps quantify the success rate of various structural prompting techniques.

#7 about 3 min

Enhancing manual debugging through model-assisted log analysis

Analyzing variable state logs alongside a language model helps developers pinpoint exact execution divergences between different runtimes.

#8 about 2 min

Addressing IP security requirements with local model deployments

Deploying open-source local models mitigates the risk of exposing sensitive proprietary codebase secrets to external APIs.

#9 about 3 min

Summary of translation reliability and audience question session

Automated code translation faces limitations that demand robust engineering pipelines and precise cross-language prompt instructions.

Matching moments

2:20 min

Reverse engineering legacy codebases with large language models

Mateusz Gren Mateusz Gren +1 · WWC Europe 2026

5:01 min

Leveraging large language models for code optimization and development

Stephan Gillich Stephan Gillich +3 · WWC 2024

46 sec

Using LLMs to reverse engineer undocumented legacy code

Michele Zuccala Michele Zuccala +4 · WWC Europe 2026

5:25 min

Addressing core challenges in large language model deployments

Vijay Krishan Gupta +1 · LIVE

3:05 min

Testing multi-agent artificial intelligence frameworks for code translation

Chris Heilmann +9 · LIVE

2:12 min

Migrating legacy source code to testable modern frameworks

Leszek Włodarski Leszek Włodarski · WWC Europe 2026

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