Out of the BLEU: How should we assess quality of the Code Generation models?-Reference-Cited by-同舟云学术

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Out of the BLEU: How should we assess quality of the Code Generation models?

Published:2023-09 Issue: Volume:203 Page:111741
ISSN:0164-1212
Container-title:Journal of Systems and Software
language:en
Short-container-title:Journal of Systems and Software

Author:

Evtikhiev Mikhail^ORCID,Bogomolov Egor^ORCID,Sokolov Yaroslav,Bryksin Timofey

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference48 articles.

1. Juice: A large scale distantly supervised dataset for open domain context-based code generation;Agashe,2019

2. JuICe: A large scale distantly supervised dataset for open domain context-based code generation;Agashe,2019

3. Hearthbreaker – open source Hearthstone simulator;Anon,2022

4. Rouge 1.5.5 perl script;Anon,2022

5. A 15 year perspective on automatic programming;Balzer;IEEE Trans. Softw. Eng.,1985

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1. Automating the correctness assessment of AI-generated code for security contexts;Journal of Systems and Software;2024-10

2. Fine-tuning Large Language Models to Improve Accuracy and Comprehensibility of Automated Code Review;ACM Transactions on Software Engineering and Methodology;2024-09-14

3. A Survey of Semantic Parsing Techniques;Symmetry;2024-09-12

4. Transformers in source code generation: A comprehensive survey;Journal of Systems Architecture;2024-08

5. Rethinking AI code generation: a one-shot correction approach based on user feedback;Automated Software Engineering;2024-07-12

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