API code recommendation using statistical learning from fine-grained changes

Author:

Nguyen Anh Tuan1,Hilton Michael2,Codoban Mihai3,Nguyen Hoan Anh1,Mast Lily4,Rademacher Eli2,Nguyen Tien N.1,Dig Danny2

Affiliation:

1. Iowa State University, USA

2. Oregon State University, USA

3. Microsoft, USA

4. University of Evansville, USA

Publisher

ACM

Cited by 110 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. Grace: Language Models Meet Code Edits;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

2. PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation;ACM Transactions on Software Engineering and Methodology;2023-11-14

3. Enhancing Code Completion with Implicit Feedback;2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS);2023-10-22

4. Measurement of Embedding Choices on Cryptographic API Completion Tasks;ACM Transactions on Software Engineering and Methodology;2023-10-17

5. ARist: An effective API argument recommendation approach;Journal of Systems and Software;2023-10

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