Improving Code Performance Using LLMs in Zero-Shot: RAPGen
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Garg:2025:ICSE,
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author = "Spandan Garg and Roshanak Zilouchian Moghaddam and
Neel Sundaresan",
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title = "Improving Code Performance Using {LLMs} in Zero-Shot:
{RAPGen}",
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booktitle = "IEEE/ACM International Conference on Software
Engineering, ICSE 2025, Software Engineering in
Practice",
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year = "2025",
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editor = "Ciera Jaspan and Rick Kazman",
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address = "Ottawa",
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month = apr # " 30-" # may # " 3",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, ANN, .NET, APR",
-
URL = "
https://arxiv.org/html/2306.17077v2",
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URL = "
https://conf.researchr.org/profile/forge-2025/spandangarg",
-
URL = "
https://conf.researchr.org/track/icse-2025/icse-2025-research-track?track=ICSE%20Research%20Track%2BICSE%20SE%20In%20Practice%20(SEIP)#program",
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abstract = "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 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 empirical
evaluation shows that RAPGen can generate performance
improvement suggestions equivalent or better than a
developer in 60 percent of the cases, getting 42
percent of them verbatim, in an expert-verified dataset
of past performance changes made by C# developers.
Furthermore, we conduct an in-the-wild evaluation to
verify the model effectiveness in practice by
suggesting fixes to developers in a large software
company. So far, we have shared performance fixes on 10
codebases that represent production services running in
the cloud and 7 of the fixes have been accepted by the
developers and integrated into the code.",
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notes = "Software Engineering in Practice SEIP
Microsoft Corporation",
- }
Genetic Programming entries for
Spandan Garg
Roshanak Zilouchian Moghaddam
Neel Sundaresan
Citations