一种基于BERT和DPCNN的Web服务分类方法

doi:10.16088/j.issn.1001-6600.2020111402

Abstract

Abstract: Web Services is an application based on the Web environment with self-adaptation, self-description, modularization, and interoperability. These features make it extremely reusable. Software reuse is a promising method to reduce software development costs. The automatic classification of Web services plays a vital role in software reuse. In recent years, machine learning techniques are widely used in service classification and have achieved some results. But the performance of traditional machine learning methods highly depends on the quality of feature engineering. This paper proposes a Bert DPCNN deep neural network model, which is based on the combination of Bert pre-training model and DPCNN deep pyramid convolutional neural network. This model can automatically extract low-level representations of service descriptions and abstract them into high-level features without feature engineering. In order to demonstrate the effectiveness of the proposed method, a comprehensive comparison is made with the traditional machine learning method and some deep neural network models on the datasets of 50 categories and 10 184 real Web services. The results show that the proposed model has higher accuracy rate than the other methods.

Key words: Web services, software reuse, machine learning, deep learning, BERT, DPCNN

CLC Number:

TP183

LU Kaifeng, YANG Yilong, LI Zhi. A Web Service Classification Method Using BERT and DPCNN[J].Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(6): 87-98.

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A Web Service Classification Method Using BERT and DPCNN

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