From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining-Reference-Cited by-同舟云学术

From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining

Published:2023-09-11 Issue: Volume: Page:
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Container-title:2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
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Author:

Ren Xiaoxue¹,Ye Xinyuan²,Zhao Dehai³,Xing Zhenchang⁴,Yang Xiaohu⁵

Affiliation:

1. School of Software Technology, Zhejiang University,Hangzhou,China

2. School of Computing, Australian National University,Canberra,Australia

3. CSIRO's Data61,Sydney,Australia

4. CSIRO's Data61 & Australian National University,Sydney,Australia

5. College of Computer Science and Technology, Zhejiang University,Hangzhou,China

Funder

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

Reference61 articles.

1. Ask me anything: A simple strategy for prompting language models;Arora;arXiv preprint,2022

2. Peer: A collaborative language model;Schick;arXiv preprint,2022

5. Selection-inference: Exploiting large language models for interpretable logical reasoning;Creswell;arXiv preprint,2022

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4. Analyzing Prompt Influence on Automated Method Generation: An Empirical Study with Copilot;Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension;2024-04-15