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Deep learning-based open API recommendation for Mashup development
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AI
DOI：10.1007/s11432-021-3531-0.png)
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Abstract
En 中文
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 TextRank 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.
Keywords:
Mashup development
open API recommendation
deep learning
neural network
service discovery
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7.6
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4.9K
Citations:
8.9K