Wuhan University Ying Shi--Home--Paper Publications

研究生毕业

武汉大学

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E-Mail:yingshi@whu.edu.cn

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PostalAddress : 武汉大学计算机学院计算机系

Email : yingshi@whu.edu.cn

Home > Scientific Research > Paper Publications

[1]. An improved knn-based efficient log anomaly detection method with automatically labeled samples[J]. ACM Transactions on Knowledge Discovery from Data (TKDD).
[2]. QLLog: A log anomaly detection method based on Q-learning algorithm[J]. Information Processing & Management.
[3]. OILog: An online incremental log keyword extraction approach based on MDP-LSTM neural network[J]. Information Systems.
[4]. Efficient performance prediction for apache spark[J]. Journal of Parallel and Distributed Computing.
[5]. Tuning configuration of apache spark on public clouds by combining multi-objective optimization and performance prediction model[J]. Journal of Systems and Software.
[6]. Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network[J]. Journal of Systems and Software.
[7]. A generative adversarial networks for log anomaly detection[J]. Computer Systems Science and Engineering.
[8]. WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation[J]. Information Sciences.
[9]. Failure analysis of static analysis software module based on big data tendency prediction[J]. Complexity.
[10]. Analysing the Impact of Scaling Out SaaS Software on Response Time[J]. Scientific Programming.
[11]. A system fault diagnosis method with a reclustering algorithm[J]. Scientific Programming,.
[12]. Diffusion Network Inference from Partial Observations[C]. Proceedings of the AAAI Conference on Artificial Intelligence..
[13]. Impact analysis about response time considering deployment change of SaaS software[J]. International Journal of Software Engineering and Knowledge Engineering.
[14]. Software defect prediction based on stacked contractive autoencoder and multi-objective optimization[J].. Computers, Materials and Continua.
[15]. HSACMA: a hierarchical scalable adaptive cloud monitoring architecture[J]. Software Quality Journal.
[16]. SaaS software performance issue diagnosis using independent component analysis and restricted Boltzmann machine[J].. Concurrency and Computation: Practice and Experience.
[17]. Anomaly detection in distributed systems via variational autoencoders[C]. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[18]. A log-based anomaly detection method with the NW ensemble rules[C]. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS).
[19]. A log-based anomaly detection method with efficient neighbor searching and automatic K neighbor selection[J]. Scientific Programming.
[20]. Database resource integration of shared cloud platform based on RAC architecture[J]. Neural Computing and Applications.
[21]. Log-based anomaly detection with the improved K-nearest neighbor[J]. International Journal of Software Engineering and Knowledge Engineering.
[22]. Within‐project and cross‐project just‐in‐time defect prediction based on denoising autoencoder and convolutional neural network[J]. IET Software.
[23]. A performance fault diagnosis method for SaaS software based on GBDT algorithm[J]. Computers, Materials & Continua.
[24]. Software defect prediction based on non-linear manifold learning and hybrid deep learning techniques[J]. Computers, Materials and Continua.
[25]. KAEA: A novel three-stage ensemble model for software defect prediction[J]. Computers, Materials and Continua.
[26]. Within-project and cross-project software defect prediction based on improved transfer naive bayes algorithm[J]. Computers, Materials and Continua.
[27]. 软件工程专业教学思考与实践[J]. 软件导刊.
[28]. Log data modeling and acquisition in supporting SaaS software performance issue diagnosis[J]. International Journal of Software Engineering and Knowledge Engineering.
[29]. Heterogeneous defect prediction with two-stage ensemble learning[J]. Automated Software Engineering.
[30]. On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction. IEEE Transactions on Software Engineering.
[31]. Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction[J].. Automated Software Engineering.
[32]. Finding similar users over multiple attributes on the basis of intuitionistic fuzzy set[J]. Mobile Networks and Applications.
[33]. Eh-recommender: Recommending exception handling strategies based on program context[C]/. 2018 23rd International Conference on Engineering of Complex Computer Systems (ICECCS).
[34]. SaaS software performance issue identification using HMRF‐MAP framework[J].. Software: Practice and Experience.
[35]. Cross-project and within-project semi-supervised software defect prediction problems study using a unified solution[C]/. 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C).
[36]. 云环境中面向服务软件的演化部署优化方法[J].. 中国科学:信息科学.
[37]. Software effort estimation based on open source projects: Case study of Github[J]. Information and Software Technology.
[38]. Model Construction and Data Management of Running Log in Supporting SaaS Software Performance Analysis[C]/. The International Conference on Software Engineering and Knowledge Engineering.

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[1]. An improved knn-based efficient log anomaly detection method with automatically labeled samples[J]. ACM Transactions on Knowledge Discovery from Data (TKDD).

[2]. QLLog: A log anomaly detection method based on Q-learning algorithm[J]. Information Processing & Management.

[3]. OILog: An online incremental log keyword extraction approach based on MDP-LSTM neural network[J]. Information Systems.

[4]. Efficient performance prediction for apache spark[J]. Journal of Parallel and Distributed Computing.

[5]. Tuning configuration of apache spark on public clouds by combining multi-objective optimization and performance prediction model[J]. Journal of Systems and Software.

[6]. Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network[J]. Journal of Systems and Software.

[7]. A generative adversarial networks for log anomaly detection[J]. Computer Systems Science and Engineering.

[8]. WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation[J]. Information Sciences.

[9]. Failure analysis of static analysis software module based on big data tendency prediction[J]. Complexity.

[10]. Analysing the Impact of Scaling Out SaaS Software on Response Time[J]. Scientific Programming.

[11]. A system fault diagnosis method with a reclustering algorithm[J]. Scientific Programming,.

[12]. Diffusion Network Inference from Partial Observations[C]. Proceedings of the AAAI Conference on Artificial Intelligence..

[13]. Impact analysis about response time considering deployment change of SaaS software[J]. International Journal of Software Engineering and Knowledge Engineering.

[14]. Software defect prediction based on stacked contractive autoencoder and multi-objective optimization[J].. Computers, Materials and Continua.

[15]. HSACMA: a hierarchical scalable adaptive cloud monitoring architecture[J]. Software Quality Journal.

[16]. SaaS software performance issue diagnosis using independent component analysis and restricted Boltzmann machine[J].. Concurrency and Computation: Practice and Experience.

[17]. Anomaly detection in distributed systems via variational autoencoders[C]. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[18]. A log-based anomaly detection method with the NW ensemble rules[C]. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS).

[19]. A log-based anomaly detection method with efficient neighbor searching and automatic K neighbor selection[J]. Scientific Programming.

[20]. Database resource integration of shared cloud platform based on RAC architecture[J]. Neural Computing and Applications.

[21]. Log-based anomaly detection with the improved K-nearest neighbor[J]. International Journal of Software Engineering and Knowledge Engineering.

[22]. Within‐project and cross‐project just‐in‐time defect prediction based on denoising autoencoder and convolutional neural network[J]. IET Software.

[23]. A performance fault diagnosis method for SaaS software based on GBDT algorithm[J]. Computers, Materials & Continua.

[24]. Software defect prediction based on non-linear manifold learning and hybrid deep learning techniques[J]. Computers, Materials and Continua.

[25]. KAEA: A novel three-stage ensemble model for software defect prediction[J]. Computers, Materials and Continua.

[26]. Within-project and cross-project software defect prediction based on improved transfer naive bayes algorithm[J]. Computers, Materials and Continua.

[27]. 软件工程专业教学思考与实践[J]. 软件导刊.

[28]. Log data modeling and acquisition in supporting SaaS software performance issue diagnosis[J]. International Journal of Software Engineering and Knowledge Engineering.

[29]. Heterogeneous defect prediction with two-stage ensemble learning[J]. Automated Software Engineering.

[30]. On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction. IEEE Transactions on Software Engineering.

[31]. Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction[J].. Automated Software Engineering.

[32]. Finding similar users over multiple attributes on the basis of intuitionistic fuzzy set[J]. Mobile Networks and Applications.

[33]. Eh-recommender: Recommending exception handling strategies based on program context[C]/. 2018 23rd International Conference on Engineering of Complex Computer Systems (ICECCS).

[34]. SaaS software performance issue identification using HMRF‐MAP framework[J].. Software: Practice and Experience.

[35]. Cross-project and within-project semi-supervised software defect prediction problems study using a unified solution[C]/. 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C).

[36]. 云环境中面向服务软件的演化部署优化方法[J].. 中国科学:信息科学.

[37]. Software effort estimation based on open source projects: Case study of Github[J]. Information and Software Technology.

[38]. Model Construction and Data Management of Running Log in Supporting SaaS Software Performance Analysis[C]/. The International Conference on Software Engineering and Knowledge Engineering.