Multiple Concurrency Anomalies Classification for Mobile Applications using Support Vector Machine
- Authors
- Wu, Zhiqiang; Abbas, Asad; Lee, Scott Uk-Jin
- Issue Date
- Aug-2017
- Publisher
- The Korea Society of Computer Information
- Keywords
- Classification; Concurrency Anomaly; Mobile Applications; Support Vector Machine (SVM)
- Citation
- The 2nd International Conference on Computing Convergence and Applications, pp.103 - 106
- Indexed
- OTHER
- Journal Title
- The 2nd International Conference on Computing Convergence and Applications
- Start Page
- 103
- End Page
- 106
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/9065
- Abstract
- Mobile applications are integral part of daily life due to their portability and convenience. In recent research, mobile applications are facing the uniqueness of approach for specific anomaly and large number of false positive. In this study, we propose Support Vector Machine (SVM) based concurrency anomaly classification approach to dynamically distinguish the status in runtime. By using anomaly classification, the approach is enabled to classify multiple concurrency anomaly with vector clocks. We proposed anomalies classification for mobile applications to detect the potential exception and reduce the false positive in runtime.
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