DPEfficR: a data and parameter efficient approach for training neural API recommendation model

Recommending application programming interfaces (APIs) is practical and essential in today’s programming landscape. An accurate API recommendation system could significantly improve developers’ coding efficiency. State-of-the-art (SOTA) API recommendation systems typically employ deep learning model...

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Bibliographic Details
Published in:	Automated software engineering Vol. 32; no. 2; p. 64
Main Authors:	Yu, Haibo, Han, Xiaohong, Chen, Simin, Feng, Xiaoning, Sun, Guangzhao, Yang, Wei
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2025 Springer Nature B.V
Subjects:	Ablation Accuracy Application programming interface Artificial Intelligence Computer Science Datasets Deep learning Effectiveness Efficiency Labeling Language Large language models Methods Modules Natural language Parameters Queries Recommender systems Run time (computers) Semantics Software Engineering/Programming and Operating Systems Tuning Use statistics Data and parameter efficient API recommendation
ISSN:	0928-8910, 1573-7535
Online Access:	Get full text
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