Context-aware API recommendation using tensor factorization  

作　　者：Yu ZHOU Chen CHEN Yongchao WANG Tingting HAN Taolue CHEN 

机构地区：[1]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]Birkbeck,University of London,London WC1E 7HX,UK [3]State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China

出　　处：《Science China(Information Sciences)》2023年第2期74-89,共16页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61972197, 61802179);Collaborative Innovation Center of Novel Software Technology and Industrialization, and Qing Lan Project;Taolue CHEN is partially supported by Birkbeck BEI School Project (EFFECT);National Natural Science Foundation of China (Grant No. 61872340);Guangdong Science and Technology Department (Grant No. 2018B010107004);Natural Science Foundation of Guangdong Province, China (Grant No. 2019A1515011689)。

摘　　要：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 contextaware 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 queryAPI-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.

关 键 词：API recommendation tensor factorization context awareness word embedding intelligent software development 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]