Security feature measurement for frequent dynamic execution paths in software system
Wang, Qian; Ren, Jiadong; Yang, Xiaoli; Cheng, Yongqiang; Davis, Darryl N.; Hu, Changzhen
Dr Yongqiang Cheng Y.Cheng@hull.ac.uk
Dr Darryl Davis D.N.Davis@hull.ac.uk
© 2018 Qian Wang et al. The scale and complexity of software systems are constantly increasing, imposing new challenges for software fault location and daily maintenance. In this paper, the Security Feature measurement algorithm of Frequent dynamic execution Paths in Software, SFFPS, is proposed to provide a basis for improving the security and reliability of software. First, the dynamic execution of a complex software system is mapped onto a complex network model and sequence model. This, combined with the invocation and dependency relationships between function nodes, fault cumulative effect, and spread effect, can be analyzed. The function node security features of the software complex network are defined and measured according to the degree distribution and global step attenuation factor. Finally, frequent software execution paths are mined and weighted, and security metrics of the frequent paths are obtained and sorted. The experimental results show that SFFPS has good time performance and scalability, and the security features of the important paths in the software can be effectively measured. This study provides a guide for the research of defect propagation, software reliability, and software integration testing.
|Journal Article Type||Article|
|Publication Date||Mar 22, 2018|
|Journal||Security and Communication Networks|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Wang, Q., Ren, J., Yang, X., Cheng, Y., Davis, D. N., & Hu, C. (2018). Security feature measurement for frequent dynamic execution paths in software system. Security and communication networks, 2018, 1-10. https://doi.org/10.1155/2018/5716878|
|Keywords||Computer Networks and Communications; Information Systems|
|Copyright Statement||© 2018 Qian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.|
© 2018 Qian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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