Learning type annotation: is big data enough?

Author:

Jesse Kevin1ORCID,Devanbu Premkumar T.1ORCID,Ahmed Toufique1

Affiliation:

1. University of California at Davis, USA

Funder

National Science Foundation

Publisher

ACM

Cited by 24 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. On the Heterophily of Program Graphs: A Case Study of Graph-based Type Inference;Proceedings of the 15th Asia-Pacific Symposium on Internetware;2024-07-24

2. Generating Python Type Annotations from Type Inference: How Far Are We?;ACM Transactions on Software Engineering and Methodology;2024-06-03

3. Data, Annotation, and Meaning-Making: The Politics of Categorization in Annotating a Dataset of Faith-based Communal Violence;The 2024 ACM Conference on Fairness, Accountability, and Transparency;2024-06-03

4. Learning Type Inference for Enhanced Dataflow Analysis;Lecture Notes in Computer Science;2024

5. DeMinify: Neural Variable Name Recovery and Type Inference;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

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