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Deep learning-based open API recommendation for Mashup development

Science China(Information Sciences) 2023年第7期66卷 94-111页

作者： Ye WANG Junwu CHEN Qiao HUANG Xin XIA Bo JIANG School of Computer and Information Engineering Zhejiang Gongshang University Software Engineering Application Technology Lab

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.

关键词： Mashup development open API recommendation deep learning neural network service discovery

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Deep learning framework for multi‐round service bundle recommendation in iterative Mashup development

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 914-930页

作者： Yutao Ma Xiao Geng Jian Wang Keqing He Dionysis Athanasopoulos School of Computer Science Wuhan UniversityWuhanChina School of Computer Science Central China Normal UniversityWuhanChina School of Electronics Electrical Engineering and Computer ScienceQueen's University BelfastBelfastUK

Recent years have witnessed the rapid development of service‐oriented computing technologies.The boom of Web services increases software developers'selection burden in developing new service‐based systems such as mashups.Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering.Existing service recom-mendation approaches are mainly designed for Mashup development in the single‐round scenario.It is hard for them to effectively update recommendation results according to developers'requirements and behaviours(e.***.instant service selection).To address this issue,the authors propose a service bundle recommendation framework based on deep learning,DLISR,which aims to capture the interactions among the target mashup to build,selected(component)services,and the following service to recommend.Moreover,an attention mechanism is employed in DLISR to weigh selected services when rec-ommending a candidate service.The authors also design two separate models for learning interactions from the perspectives of content and invocation history,respectively,and a hybrid model called HISR.Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.

关键词： attention deep learning Mashup development recommender systems service bundle

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