Michael Niebisch
Leveraging Large Language Models for Legacy Code Translation: Challenges and Solutions
#1about 5 minutes
Motivations for translating legacy MATLAB code to Python
The project aimed to explore LLMs for modernizing a large, legacy MATLAB codebase due to the scarcity of MATLAB developers and the rise of Python.
#2about 4 minutes
Using a semi-automatic workflow with ChatGPT for translation
The initial approach involved a manual copy-paste workflow using the ChatGPT web interface, which saved time on boilerplate but struggled with large code chunks and introduced errors.
#3about 4 minutes
Overcoming language-specific challenges in code translation
Key translation challenges arose from fundamental differences between MATLAB and Python, such as array indexing and memory layout, requiring a divide-and-conquer approach and robust unit tests.
#4about 5 minutes
Developing an automated pipeline for translation and auto-fixing
To improve efficiency, an automated pipeline was built to first annotate code with type and shape information before translation and then use an agent-based tool to automatically fix bugs based on test failures.
#5about 4 minutes
Evaluating LLM performance and providing debugging support
A framework was developed to evaluate translation quality by testing against known failure cases, and a debugging tool uses LLMs to compare execution logs from both languages to pinpoint errors.
#6about 3 minutes
Considering local LLMs for security and summarizing key learnings
Due to IP and security concerns with cloud APIs, local models like Llama 2 were explored, and the project concluded that while LLMs are promising tools, fully automated, error-free translation remains a significant challenge.
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