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论文:2021,Vol:39,Issue(5):1049-1056 |
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引用本文： |
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成静, 王威, 帅正义. 基于深度学习的移动应用众包测试智能推荐算法[J]. 西北工业大学学报 |
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CHENG Jing, WANG Wei, SHUAI Zhengyi. Intelligent recommendation algorithm of mobile application crowdsourcing test based on deep learning[J]. Northwestern polytechnical university |
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| 基于深度学习的移动应用众包测试智能推荐算法 |
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| 成静1, 王威2, 帅正义2 |
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1. 西安工业大学 计算机科学与工程学院, 陕西 西安 710021;
2. 西北工业大学 软件学院, 陕西 西安 710072 |
| 摘要: |
| 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。 |
| 关键词:
深度学习
堆叠边缘降噪自动编码器
词向量
推荐算法
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| Intelligent recommendation algorithm of mobile application crowdsourcing test based on deep learning |
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| CHENG Jing1, WANG Wei2, SHUAI Zhengyi2 |
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1. 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 |
| Abstract: |
| 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. |
| Key words:
deep learning
stacked edge denoising autoencoders
word vector
recommendation algorithms
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| 收稿日期: 2021-01-16
修回日期:
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| DOI: 10.1051/jnwpu/20213951049 |
| 基金项目: 陕西省自然科学基础研究计划（2019JM-484）及陕西省教育厅基金项目（19JK0414）资助 |
| 通讯作者:
Email： |
| 作者简介: 成静(1982-),西安工业大学讲师,主要从事移动应用测试、智能测试研究。
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| 作者相关文章 |
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| 成静 在本刊中的所有文章 |
| 王威 在本刊中的所有文章 |
| 帅正义 在本刊中的所有文章 |
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参考文献: |
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[1] LEICHT N, BLOHM I, LEIMEISTER J M. Leveraging the power of the crowd for software testing[J]. IEEE Software, 2017, 34(2):62-69
[2] CHENG H T, KOC L, HARMSEN J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016:7-10
[3] GUO H F, TANG R M, YE Y M, et al. DeepFM:a factorization-machine based neural network for CTR prediction[C]//26th Int Joint Conf Artificial Intelligence, Melbourne, Australia, 2017
[4] LIAN J, ZHOU X, ZHANG F. Xdeepfm:combining explicit and implicit feature interactions for recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018:1754-1763
[5] 张政馗,庞为光,谢文静, 等. 面向实时应用的深度学习研究综述[J]. 软件学报,2020,31(9):2654-2677 ZHANG Zhengkui, PANG Weiguang, XIE Wenjing, et al. Deep learning for real-time applications:a survey[J]. Journal of Software, 2020, 31(9):2654-2677(in Chinese)
[6] HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web, 2018:173-182
[7] LI S, KAWALE J, FU Y. Deep collaborative filtering via marginalized denoising auto-encoder[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015:811-820
[8] WANG H, WANG N, YEUNG D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015:1235-1244
[9] ZHANG Shuai, YAO Lina, XU Xiwei. AutoSVD++:an efficient hybrid collaborative filtering model via contractive auto-encoders[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017:957-960
[10] WEI J, HE J, CHEN K, et al.Collaborative filtering and deep learning base recommendation system for cold start items[J]. Expert Systems with Applications, 2017, 69:29-39
[11] FORNACCIARI P, GUIDI B, MORDONINI M, et al. Guess the movie-linking facebook pages to IMDb movies[C]//Internationa Workshop on Personal Analytics and Privacy, 2017:98-109 |
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