RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot

• Main Authors: Garg, Spandan, Moghaddam, Roshanak Zilouchian, Sundaresan, Neel
• Format: Journal Article
• Language: English
• Published: 29-06-2023
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Software Engineering
• DOI: 10.48550/arxiv.2306.17077

Performance bugs are non-functional bugs that can even manifest in
well-tested commercial products. Fixing these performance bugs is an important
yet challenging problem. In this work, we address this challenge and present a
new approach called Retrieval-Augmented Prompt Generation (RAPGen). Given a
code snippet with a performance issue, RAPGen first retrieves a prompt
instruction from a pre-constructed knowledge-base of previous performance bug
fixes and then generates a prompt using the retrieved instruction. It then uses
this prompt on a Large Language Model (such as Codex) in zero-shot to generate
a fix. We compare our approach with the various prompt variations and state of
the art methods in the task of performance bug fixing. Our evaluation shows
that RAPGen can generate performance improvement suggestions equivalent or
better than a developer in ~60% of the cases, getting ~42% of them verbatim, in
an expert-verified dataset of past performance changes made by C# developers.