Context-aware API recommendation using tensor factorization

An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces (APIs). Contextual information is widely recognized to play a crucial role in effective API recommendation, but it is largely overlooked in practice. In this paper, we propose...

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Vydané v:Science China. Information sciences Ročník 66; číslo 2; s. 122101
Hlavní autori: Zhou, Yu, Chen, Chen, Wang, Yongchao, Han, Tingting, Chen, Taolue
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Beijing Science China Press 01.02.2023
Springer Nature B.V
Predmet:
Algorithms
Application programming interface
Computer Science
Context
Factorization
Harnesses
Information Systems and Communication Service
Java
Mathematical analysis
Performance evaluation
Performance measurement
Programmers
Queries
Research Paper
Semantics
Software
Software development
Tensors
API recommendation
context awareness
tensor factorization
word embedding
intelligent software development
ISSN:1674-733X, 1869-1919
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Shrnutí:An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces (APIs). Contextual information is widely recognized to play a crucial role in effective API recommendation, but it is largely overlooked in practice. In this paper, we propose context-aware API recommendation using tensor factorization (CARTF), a novel API recommendation approach in considering programmers’ working context. To this end, we use tensors to explicitly represent the query-API-context triadic relation. When a new query is made, CARTF harnesses word embeddings to retrieve similar user queries, based on which a third-order tensor is constructed. CARTF then applies non-negative tensor factorization to complete missing values in the tensor and the Smith-Waterman algorithm to identify the most matched context. Finally, the ranking of the candidate APIs can be derived based on which API sequences are recommended. Our evaluation confirms the effectiveness of CARTF for class-level and method-level API recommendations, outperforming state-of-the-art baseline approaches against a number of performance metrics, including SuccessRate, Precision, and Recall.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-021-3529-9