Conference Paper

Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review

Abstract

With the rapid development of Large Language Models (LLMs), a large number of machine learning models have been developed to assist programming tasks including the generation of program code from natural language input. However, how to evaluate such LLMs for this task is still an open problem despite of the great amount of research efforts that have been made and reported to evaluate and compare them. This paper provides a critical review of the existing work on the testing and evaluation of these tools with a focus on two key aspects: the benchmarks and the metrics used in the evaluations. Based on the review, further research directions are discussed.

Authors

Paul, Debalina Ghosh
Zhu, Hong
Bayley, Ian

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2024
Date of RADAR deposit: 2024-06-19

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Related resources

This RADAR resource is the Accepted Manuscript of [arXiv preprint, version 1] Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review
This RADAR resource is the Accepted Manuscript of Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review