2021 7th International Symposium on System and Software Reliability (ISSSR)最新文献

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Automatic Multi-steps Prediction Modelling for Wind Power Forecasting 风电预测的自动多步预测模型
2021 7th International Symposium on System and Software Reliability (ISSSR) Pub Date : 2021-09-01 DOI: 10.1109/ISSSR53171.2021.00018
Shuwen Zheng, Jie Liu
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引用次数: 0
Practical Application of Improving the System Reliability and Stability via Microservices Architecture 通过微服务架构提高系统可靠性和稳定性的实际应用
2021 7th International Symposium on System and Software Reliability (ISSSR) Pub Date : 2021-09-01 DOI: 10.1109/ISSSR53171.2021.00023
Liu Mengxu
{"title":"Practical Application of Improving the System Reliability and Stability via Microservices Architecture","authors":"Liu Mengxu","doi":"10.1109/ISSSR53171.2021.00023","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00023","url":null,"abstract":"The system architecture of the software is one of the decisive factors. In this article, we propose a solution based on a microservices architecture that greatly improves the reliability and stability of the system and used it to solve the problems encountered by the Credit Information sharing platform of Henan Province. It is the first case of upgrading a provinciallevel similar platform based on the new technical architecture in China. After more than one year of formal operation, it has achieved the expected goal. At present, the total amount of credit information of the platform is more than 5 billion, and the number of queries is more than 350 million. The reliability and stability of the system are excellent. We can be sure that the appropriate application of microservices architecture can greatly improve the reliability and stability of the system.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128252158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mutation Operator Reduction for Deep Learning System 深度学习系统的突变算子约简
2021 7th International Symposium on System and Software Reliability (ISSSR) Pub Date : 2021-09-01 DOI: 10.1109/ISSSR53171.2021.00014
Shiyu Zhang, Xingya Wang, Lichao Feng, Zhihong Zhao
{"title":"Mutation Operator Reduction for Deep Learning System","authors":"Shiyu Zhang, Xingya Wang, Lichao Feng, Zhihong Zhao","doi":"10.1109/ISSSR53171.2021.00014","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00014","url":null,"abstract":"The mutation testing method of Deep Learning (DL) system proposes a series of DL mutation operators, but the same as traditional software mutation testing methods, a large number of mutants will be generated during the testing process, which will cause huge costs. The traditional mutation operator reduction method is based on source program business logic. Owe to the fundamental difference between traditional system and DL system, traditional reduction methods cannot be directly applied to the DL mutation operators. In this paper, we propose the mutation operator reduction method for DL system, which can be divided into three steps. It firstly classifies all mutation operators by the scope of action of them. Then, it combines different classes of mutation operators. Finally, it analyzes the mutation score of different mutation operators combinations to obtain a sufficient mutation operators subset. This method has been tested on the MNIST datasets and the LENET-5 model. The experimental results shows that the number of mutants reduced by 41.67%, which effectively proved that our reduction method can effectively reduce the number of mutants generated, reduce the testing cost, and improve the accuracy of the mutation score.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132341527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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