CodeSift: An LLM-Based Reference-Less Framework for Automatic Code Validation

The advent of large language models (LLMs) has greatly facilitated code generation, but ensuring the functional correctness of generated code remains a challenge. Traditional validation methods are often time-consuming, error-prone, and impractical for large volumes of code. We introduce CodeSift, a...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Aggarwal, Pooja, Chatterjee, Oishik, Dai, Ting, Mohapatra, Prateeti, Paulovicks, Brent, Blancett, Brad, De Magalhaes, Arthur
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.08.2024
Subjects
Effectiveness
Error correction
Large language models
Programming languages
State-of-the-art reviews
Online AccessGet full text

Cover

More Information
Summary:The advent of large language models (LLMs) has greatly facilitated code generation, but ensuring the functional correctness of generated code remains a challenge. Traditional validation methods are often time-consuming, error-prone, and impractical for large volumes of code. We introduce CodeSift, a novel framework that leverages LLMs as the first-line filter of code validation without the need for execution, reference code, or human feedback, thereby reducing the validation effort. We assess the effectiveness of our method across three diverse datasets encompassing two programming languages. Our results indicate that CodeSift outperforms state-of-the-art code evaluation methods. Internal testing conducted with subject matter experts reveals that the output generated by CodeSift is in line with human preference, reinforcing its effectiveness as a dependable automated code validation tool.
ISSN:2331-8422