A Method of Predicting Software Behavior Risk based on Off-line Runtime Verification

DOI

10.2991/icmmct-16.2016.232How to use a DOI?

Keywords

software behavior risk; runtime verification; prediction

Abstract

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/icmmct-16.2016.232
ID  - Hu2016/03
ER  -