A Method of Predicting Software Behavior Risk based on Off-line Runtime Verification
Lei Hu, Guohua Jiang
Available Online March 2016.
- https://doi.org/10.2991/icmmct-16.2016.232How to use a DOI?
- software behavior risk; runtime verification; prediction
- The current methods of software behavior risk prediction is mainly through the study of the operating rules from the data of the other software of the same type, and that leads to differences between the prediction results and the actual software behavior. Aiming at this problem, this paper presents a software behavior prediction method, which combines prediction of software behavior with runtime verification, using Markov Chain and Hidden Markov Model(HMM), to analys the data from offline runtime verification and predict software behavior. Experiments show that this method can significantly improve the accuracy of the prediction.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Lei Hu AU - Guohua Jiang PY - 2016/03 DA - 2016/03 TI - A Method of Predicting Software Behavior Risk based on Off-line Runtime Verification BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology PB - Atlantis Press SP - 1179 EP - 1183 SN - 2352-5401 UR - https://doi.org/10.2991/icmmct-16.2016.232 DO - https://doi.org/10.2991/icmmct-16.2016.232 ID - Hu2016/03 ER -