Out of the Bleu: How Should We Assess Quality of the Code Generation Models?-Reference-Cited by-同舟云学术

Out of the Bleu: How Should We Assess Quality of the Code Generation Models?

Published:2022 Issue: Volume: Page:
ISSN:1556-5068
Container-title:SSRN Electronic Journal
language:en
Short-container-title:SSRN Journal

Author:

Evtikhiev Mikhail,Bogomolov Egor,Sokolov Yaroslav,Bryksin Timofey

Publisher

Elsevier BV

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference46 articles.

1. JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation;Rajas Agashe;Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP),2019

2. A 15 year perspective on automatic programming;Robert Balzer;IEEE Transactions on Software Engineering,1985

3. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments;Satanjeev Banerjee;Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization,2005

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1. An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

2. SkCoder: A Sketch-based Approach for Automatic Code Generation;2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE);2023-05

3. “What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models;Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19