Deep learning-based open API recommendation for Mashup development
作者机构：School of Computer and Information Engineering Zhejiang Gongshang University Software Engineering Application Technology Lab
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2023年第66卷第7期
页 面：94-111页
核心收录：
学科分类：0710[理学-生物学] 1205[管理学-图书情报与档案管理] 08[工学] 081104[工学-模式识别与智能系统] 081203[工学-计算机应用技术] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by National Science Foundation of Zhejiang Province (Grant Nos. LY21F020011, LY20F020027, LY19F020003) Key Research and Development Program of Zhejiang Province (Grant No. 2021C01162) National Natural Science Foundation of China (Grant No. 61672459)
主 题：Mashup development open API recommendation deep learning neural network service discovery
摘 要：Mashup developers often need to find open application programming interfaces(APIs) for their composition application development. Although most enterprises and service organizations have encapsulated their businesses or resources online as open APIs, finding the right high-quality open APIs is not an easy task from a library with several open APIs. To solve this problem, this paper proposes a deep learning-based open API recommendation(DLOAR) approach. First, the hierarchical density-based spatial clustering of applications with a noise topic model is constructed to build topic models for Mashup clusters. Second,developers’ requirement keywords are extracted by the Text Rank algorithm, and the language model is built. Third, a neural network-based three-level similarity calculation is performed to find the most relevant open APIs. Finally, we complement the relevant information of open APIs in the recommended list to help developers make better choices. We evaluate the DLOAR approach on a real dataset and compare it with commonly used open API recommendation approaches: term frequency-inverse document frequency, latent dirichlet allocation, Word2Vec, and Sentence-BERT. The results show that the DLOAR approach has better performance than the other approaches in terms of precision, recall, F1-measure, mean average precision,and mean reciprocal rank.
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