The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Michael R Lyu - One of the best experts on this subject based on the ideXlab platform.
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Context-Aware Reliability Prediction of Black-Box Services.
arXiv: Software Engineering, 2015Co-Authors: Jieming Zhu, Zibin Zheng, Michael R LyuAbstract:Reliability Prediction is an important research problem in software Reliability engineering, which has been widely studied in the last decades. However, modelling and predicting user-perceived Reliability of black-box services remain an open problem. Software services, such as Web services and Web APIs, generally provide black-box functionalities to users through the Internet, leading to a lack of their internal information for Reliability analysis. Furthermore, the user-perceived service Reliability depends not only on the service itself, but also heavily on the invocation context (e.g., service workloads, network conditions), whereby traditional Reliability models become ineffective and inappropriate. To address these new challenges posed by black-box services, in this paper, we propose CARP, a context-aware Reliability Prediction approach that leverages historical usage data from users for Reliability Prediction. Through context-aware model construction and Prediction, CARP is able to alleviate the data sparsity problem that heavily limits the Prediction accuracy of other existing approaches. The preliminary evaluation results show that CARP can make significant improvement on Reliability Prediction accuracy, e.g., 41% for MAE and 38% for RMSE when only 5% of data are available.
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Personalized Reliability Prediction of Web Services
ACM Transactions on Software Engineering and Methodology, 2013Co-Authors: Zibin Zheng, Michael R LyuAbstract:Service Oriented Architecture (SOA) is a business-centric IT architectural approach for building distributed systems. Reliability of service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet connections. Designing efficient and effective Reliability Prediction approaches of Web services has become an important research issue. In this article, we propose two personalized Reliability Prediction approaches of Web services, that is, neighborhood-based approach and model-based approach. The neighborhood-based approach employs past failure data of similar neighbors (either service users or Web services) to predict the Web service Reliability. On the other hand, the model-based approach fits a factor model based on the available Web service failure data and use this factor model to make further Reliability Prediction. Extensive experiments are conducted with our real-world Web service datasets, which include about 23 millions invocation results on more than 3,000 real-world Web services. The experimental results show that our proposed Reliability Prediction approaches obtain better Reliability Prediction accuracy than other competing approaches.
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ICSE (1) - Collaborative Reliability Prediction of service-oriented systems
Proceedings of the 32nd ACM IEEE International Conference on Software Engineering - ICSE '10, 2010Co-Authors: Zibin Zheng, Michael R LyuAbstract:Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate Reliability Prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative Reliability Prediction approach, which employs the past failure data of other similar users to predict the Web service Reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a Reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative Reliability Prediction approach obtains better Reliability Prediction accuracy than other approaches.
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Collaborative Reliability Prediction of service-oriented systems
Proceedings of the 32nd ACM IEEE International Conference on Software Engineering - ICSE '10, 2010Co-Authors: Zibin Zheng, Michael R LyuAbstract:Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate Reliability Prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative Reliability Prediction approach, which employs the past failure data of other similar users to predict the Web service Reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a Reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative Reliability Prediction approach obtains better Reliability Prediction accuracy than other approaches.
Zibin Zheng - One of the best experts on this subject based on the ideXlab platform.
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Context-Aware Reliability Prediction of Black-Box Services.
arXiv: Software Engineering, 2015Co-Authors: Jieming Zhu, Zibin Zheng, Michael R LyuAbstract:Reliability Prediction is an important research problem in software Reliability engineering, which has been widely studied in the last decades. However, modelling and predicting user-perceived Reliability of black-box services remain an open problem. Software services, such as Web services and Web APIs, generally provide black-box functionalities to users through the Internet, leading to a lack of their internal information for Reliability analysis. Furthermore, the user-perceived service Reliability depends not only on the service itself, but also heavily on the invocation context (e.g., service workloads, network conditions), whereby traditional Reliability models become ineffective and inappropriate. To address these new challenges posed by black-box services, in this paper, we propose CARP, a context-aware Reliability Prediction approach that leverages historical usage data from users for Reliability Prediction. Through context-aware model construction and Prediction, CARP is able to alleviate the data sparsity problem that heavily limits the Prediction accuracy of other existing approaches. The preliminary evaluation results show that CARP can make significant improvement on Reliability Prediction accuracy, e.g., 41% for MAE and 38% for RMSE when only 5% of data are available.
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Personalized Reliability Prediction of Web Services
ACM Transactions on Software Engineering and Methodology, 2013Co-Authors: Zibin Zheng, Michael R LyuAbstract:Service Oriented Architecture (SOA) is a business-centric IT architectural approach for building distributed systems. Reliability of service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet connections. Designing efficient and effective Reliability Prediction approaches of Web services has become an important research issue. In this article, we propose two personalized Reliability Prediction approaches of Web services, that is, neighborhood-based approach and model-based approach. The neighborhood-based approach employs past failure data of similar neighbors (either service users or Web services) to predict the Web service Reliability. On the other hand, the model-based approach fits a factor model based on the available Web service failure data and use this factor model to make further Reliability Prediction. Extensive experiments are conducted with our real-world Web service datasets, which include about 23 millions invocation results on more than 3,000 real-world Web services. The experimental results show that our proposed Reliability Prediction approaches obtain better Reliability Prediction accuracy than other competing approaches.
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ICSE (1) - Collaborative Reliability Prediction of service-oriented systems
Proceedings of the 32nd ACM IEEE International Conference on Software Engineering - ICSE '10, 2010Co-Authors: Zibin Zheng, Michael R LyuAbstract:Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate Reliability Prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative Reliability Prediction approach, which employs the past failure data of other similar users to predict the Web service Reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a Reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative Reliability Prediction approach obtains better Reliability Prediction accuracy than other approaches.
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Collaborative Reliability Prediction of service-oriented systems
Proceedings of the 32nd ACM IEEE International Conference on Software Engineering - ICSE '10, 2010Co-Authors: Zibin Zheng, Michael R LyuAbstract:Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate Reliability Prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative Reliability Prediction approach, which employs the past failure data of other similar users to predict the Web service Reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a Reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative Reliability Prediction approach obtains better Reliability Prediction accuracy than other approaches.
D.w. Coit - One of the best experts on this subject based on the ideXlab platform.
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Prioritizing system-Reliability Prediction improvements
IEEE Transactions on Reliability, 2001Co-Authors: D.w. CoitAbstract:This method prioritizes system-Reliability Prediction activities once a preliminary Reliability-Prediction has been made. System-Reliability Predictions often use data and models from a variety of sources, each with differing degrees of estimation uncertainty. Since time and budgetary constraints limit the extent of analyzes and testing needed to estimate component Reliability, it is necessary to allocate limited resources intelligently. A Reliability-Prediction prioritization index (RPPI) is defined to provide a relative ranking of components based on their potential for improving the accuracy of a system-level Reliability Prediction by decreasing the variance of the system-Reliability estimate. If a component has a high RPPI, then additional testing or analysis should be considered to decrease the variance of the component Reliability estimate. RPPI is based on a decomposition of the variance of the system-Reliability or on a mean-time-to-failure estimate. Using these indexes, the effect of individual components within the system can be compared, ranked, and assigned to priority groups. The ranking is based on whether a decrease of the component-Reliability estimate variance meaningfully decreases the system-Reliability estimate variance. The procedure is demonstrated with two examples.
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System Reliability Prediction prioritization strategy
Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055), 2000Co-Authors: D.w. CoitAbstract:A procedure is presented to prioritize system Reliability Prediction activities once a preliminary Reliability Prediction has been determined. Time and budgetary constraints impose limitations on the extent of analyses and testing needed to determine component Reliability estimates. Therefore, it becomes necessary to allocate limited resources in accordance with their system-level impact. The variance of the system Reliability estimate is decomposed so that the effect of individual components within the system can be compared and ranked. The Reliability Prediction prioritization index (RPPI) is defined to allow a comparison of components and to provide a relative ranking of components that can be used to separate the components into two priority groups. The separation is based on whether a decrease of the component Reliability estimate variance meaningfully decreases the system Reliability estimate variance. The procedure is demonstrated on an automatic train control example.
Carol S. Smidts - One of the best experts on this subject based on the ideXlab platform.
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An automated software Reliability Prediction system for safety critical software
Empirical Software Engineering, 2016Co-Authors: Xiang Li, Chetan Mutha, Carol S. SmidtsAbstract:Software Reliability is one of the most important software quality indicators. It is concerned with the probability that the software can execute without any unintended behavior in a given environment. In previous research we developed the Reliability Prediction System (RePS) methodology to predict the Reliability of safety critical software such as those used in the nuclear industry. A RePS methodology relates the software engineering measures to software Reliability using various models, and it was found that RePS’s using Extended Finite State Machine (EFSM) models and fault data collected through various software engineering measures possess the most satisfying Prediction capability. In this research the EFSM-based RePS methodology is improved and implemented into a tool called Automated Reliability Prediction System (ARPS). The features of the ARPS tool are introduced with a simple case study. An experiment using human subjects was also conducted to evaluate the usability of the tool, and the results demonstrate that the ARPS tool can indeed help the analyst apply the EFSM-based RePS methodology with less number of errors and lower error criticality.
Jian-bo Yang - One of the best experts on this subject based on the ideXlab platform.
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Dynamic evidential reasoning algorithm for systems Reliability Prediction
International Journal of Systems Science, 2010Co-Authors: Jian-bo YangAbstract:In this article, dynamic evidential reasoning (DER) algorithm is applied to forecast Reliability in turbochargers engine systems and a Reliability Prediction model is developed. The focus of this study is to examine the feasibility and validity of DER algorithm in systems Reliability Prediction by comparing it with some existing approaches. To build an effective DER forecasting model, the parameters of Prediction model must be set carefully. To solve this problem, a generic nonlinear optimisation model is investigated to search for the optimal parameters of forecasting model, and then the optimal parameters are adopted to construct the DER forecasting model. Finally, a numerical example is provided to demonstrate the detailed implementation procedures and the validity of the proposed approach in the areas of Reliability Prediction.
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system Reliability Prediction model based on evidential reasoning algorithm with nonlinear optimization
Expert Systems With Applications, 2010Co-Authors: Jian-bo YangAbstract:In this paper, a novel Reliability Prediction technique based on the evidential reasoning (ER) algorithm is developed and applied to forecast Reliability in turbocharger engine systems. The focus of this study is to examine the feasibility and validity of the ER algorithm in systems Reliability Prediction by comparing it with some existing approaches. To determine the parameters of the proposed model accurately, some nonlinear optimization models are investigated to search for the optimal parameters of forecasting model by minimizing the mean square error (MSE) criterion. Finally, a numerical example is provided to demonstrate the detailed implementation procedures. The experimental results show that the Prediction performance of the ER-based Prediction model outperforms several existing methods in terms of Prediction accuracy or speed.
