Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code

Author:

Huang Qing1ORCID,Yuan Zhiqiang1ORCID,Xing Zhenchang2ORCID,Xu Xiwei3ORCID,Zhu Liming4ORCID,Lu Qinghua3ORCID

Affiliation:

1. School of Computer Information Engineering, Jiangxi Normal University, China

2. CSIRO's Data61 & Australian National University, Australia

3. CSIRO's Data61, Australia

4. CSIRO's Data61 & School of CSE, UNSW, Australia

Funder

the National Nature Science Foundation of China under Grant

Postgraduate Innovation Fund Project of Jiangxi Province

the Nature Science Foundation of Jiangxi Province

the Science and technology Key project of Education Department of Jiangxi Province

Publisher

ACM

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prompting Is All You Need: Automated Android Bug Replay with Large Language Models;Proceedings of the 46th IEEE/ACM International Conference on Software Engineering;2024-02-06

2. FQN Inference in Partial Code by Prompt-tuned Language Model of Code;ACM Transactions on Software Engineering and Methodology;2023-12-21

3. Statistical Type Inference for Incomplete Programs;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

4. API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model;ACM Transactions on Software Engineering and Methodology;2023-11-23

5. Prompt Tuning in Code Intelligence: An Experimental Evaluation;IEEE Transactions on Software Engineering;2023-11

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