Volume 39, Number 5, October 2021
|Page(s)||1049 - 1056|
|Published online||14 December 2021|
Intelligent recommendation algorithm of mobile application crowdsourcing test based on deep learning
School of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China
2 School of Software, Northwestern Polytechnical University, Xi'an 710072, China
As the functions of mobile applications become more and more complex, the crowdsourcing testing puts higher demands on the professional skills of testers. Therefore, it is an important factor to ensure test quality how to effectively match test task requirements with test personnel's skill level and achieve accurate crowdsourcing test task recommendation. This paper proposes a crowdsourcing test task recommendation algorithm for mobile applications based on deep learning. Firstly, feature analysis is carried out for testing tasks and testers, and feature systems are designed respectively. Second, the resulting characteristic data is used as input data for the Stacked Marginalized Denoising Autoencoder (SMDA). The deep feature data learned from SMDA are combined as the input of Deep Neural Networks (DNN). Finally, the learning ability of DNN is used for prediction. Experimental results show that the proposed algorithm has obvious advantages in both performance and training time compared with CDL and AUTOSVD ++, which verifies the effectiveness of the proposed algorithm. The proposed algorithm can recommend testing tasks to appropriate testers and improve the precision of the algorithm.
随着移动应用功能日趋复杂，众包测试对测试人员的专业技能提出更高要求。因此，如何高效匹配测试任务需求与测试人员技能水平，实现精准的众包测试任务推荐是保证测试质量的重要因素。提出一种基于深度学习的移动应用众包测试任务推荐算法。针对测试任务和测试人员进行特征分析，分别设计特征体系；将得到的特征数据作为堆叠式边缘降噪自动编码器（stacked marginalized denoising autoencoder，SMDA）输入数据，将SMDA学习到的深层特征数据结合作为深度神经网络（deep neural networks，DNN）的输入；利用DNN的学习能力进行预测。实验结果表明：所提算法相较于CDL和AutoSVD++等算法无论是性能还是训练时间都有明显优势，验证了算法的有效性。所提算法可以将测试任务推荐给适合的测试人员并提高了推荐算法的精细度。
Key words: deep learning / stacked edge denoising autoencoders / word vector / recommendation algorithms
关键字 : 深度学习 / 堆叠边缘降噪自动编码器 / 词向量 / 推荐算法
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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