Summary of Leveraging Llms For Legacy Code Modernization: Challenges and Opportunities For Llm-generated Documentation, by Colin Diggs et al.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

by Colin Diggs, Michael Doyle, Amit Madan, Siggy Scott, Emily Escamilla, Jacob Zimmer, Naveed Nekoo, Paul Ursino, Michael Bartholf, Zachary Robin, Anand Patel, Chris Glasz, William Macke, Paul Kirk, Jasper Phillips, Arun Sridharan, Doug Wendt, Scott Rosen, Nitin Naik, Justin F. Brunelle, Samruddhi Thaker

First submitted to arxiv on: 22 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper explores the use of Large Language Models (LLMs) to generate documentation for legacy software systems written in outdated languages like MUMPS and mainframe assembly. The authors propose a prompting strategy and evaluation rubric to assess the quality of generated code comments, focusing on completeness, readability, usefulness, and hallucination. The study uses two datasets: an electronic health records (EHR) system in MUMPS and open-source applications in IBM mainframe Assembly Language Code (ALC). Results show that LLM-generated comments are generally accurate, complete, readable, and useful for both languages, although ALC poses unique challenges. However, no automated metrics strongly correlate with comment quality to predict or measure LLM performance.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper is about using computers to help understand old software code. Old code can be tricky to maintain because it was written a long time ago in strange languages. Researchers want to see if special computer models called Large Language Models (LLMs) can help by generating notes and comments for the code. They tested these models on two types of old code: one from an electronic health records system, and another from IBM’s mainframe computers. The results show that the LLM-generated notes are usually good, accurate, and easy to understand for both types of code. However, they found some challenges when using the models with ALC (Assembly Language Code) code.