Title: Service component recommendation based on LSTM

Authors: Xiao Yang; Hong Xu; Hongping Shu; Yaqiang Wang; Kui Liu; Yuan Ho

Addresses: School of Computer Science, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest Airport Economic Development Zone, Chengdu, Sichuan, China ' School of Computer Science, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest Airport Economic Development Zone, Chengdu, Sichuan, China ' School of Software Engineering, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest Airport Economic Development Zone, Chengdu, Sichuan, China ' School of Software Engineering, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest Airport Economic Development Zone, Chengdu, Sichuan, China ' School of Software Engineering, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest Airport Economic Development Zone, Chengdu, Sichuan, China ' National Center for Atmospheric Research, Boulder, Denver, Colorado, USA

Abstract: Service component selection is a core problem in software development process. With an enormous number of components available, it is often difficult for the developer to select the most appropriate one, as he or she might not be aware of all the possible business scenes ahead of time. Taking these challenges into consideration, we propose a deep learning-based system that automatically recommends service components based on component selection history during the software development process. We employ a sequential model with two long short-term memory (LSTM) layers and two fully connected layers, using SoftMax as an activation function, to predict the next service component. The model was trained, validated and tested on dataset with more than 120,000 examples from a real-world software company. The proposed network outperforms the baseline methods in terms of the evaluation criteria. In addition, the model results were deployed in a real-world software tool and gave positive feedback.

Keywords: service component; recommendation system; long short-term memory network.

DOI: 10.1504/IJES.2021.10036385

International Journal of Embedded Systems, 2021 Vol.14 No.2, pp.201 - 209

Received: 27 Mar 2019
Accepted: 25 Jan 2020

Published online: 31 Mar 2021 *

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