HE Haitao, SHAN Chun, HE Hongdou, et al., “OSFPMiner: An Optimal Weighted Traversal Software Patterns Miner Based on Complex Network,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 255-264, 2020, doi: 10.1049/cje.2020.01.002
Citation: HE Haitao, SHAN Chun, HE Hongdou, et al., “OSFPMiner: An Optimal Weighted Traversal Software Patterns Miner Based on Complex Network,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 255-264, 2020, doi: 10.1049/cje.2020.01.002

OSFPMiner: An Optimal Weighted Traversal Software Patterns Miner Based on Complex Network

doi: 10.1049/cje.2020.01.002
Funds:  This work is supported by the National Key R&D Program of China (No.2016YFB0800700), the National Natural Science Foundation of China (No.61772449, No.61572420, No.61772451, No.61807028, No.61802332) and the Natural Science Foundation of Hebei Province, China (No.F2019203120).
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  • Corresponding author: SHAN Chun (corresponding author) received her Ph.D. degree in Computer Science from Beijing Institute of Technology in 2015. She is an associate professor and master tutor of school of computer science in Beijing Institute of Technology. Her research interests include Software Security, Network Security and Artificial Intelligence. (Email:sherryshan@bit.edu.cn)
  • Received Date: 2019-02-15
  • Rev Recd Date: 2019-12-27
  • Publish Date: 2020-03-10
  • The weighted traversal pattern is important in software system for a better understanding of the internal structure and behavior of software. To mine important patterns of software, a complex network-based Optimal Software Fault Patterns Miner is presented. By analyzing the multiple execution traces of software and the relations among functions, we establish the Weighted Software Execution Dependency Graph model ultimately. The traversal database is generated through depth-first search strategy and the extraction of software path traversals. According to the downward-closure property, a pruning strategy is adopted by Weighted Frequent Candidate Pattern Tree to cut off more unpromising patterns in advance. A set of important patterns is derived without repeated calculation. The experimental results show that the proposed approach has good performance in the number of weighted frequent candidate patterns and time efficiency.
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