TY - GEN
T1 - Mining important nodes in complex software network based on ripple effects of probability
AU - Ren, Jiadong
AU - Huang, Guoyan
AU - Wang, Qian
AU - He, Haitao
AU - Liu, Xinqian
AU - Zhao, Xiaolin
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/5/17
Y1 - 2019/5/17
N2 - The complexity of software directly leads to an increasing cost in software testing and maintenance. Finding the important nodes with significant vulnerability is helpful for fault discovery and further reduces the damage to the software system. In this paper, a new algorithm named MIN-REP (Mining the Important Nodes based on Ripple Effects of Probability) is proposed to find out the paths with greater possibility for fault propagation, and then the important nodes are mined. To build a model of directed unweighted software network, functions are taken as the nodes and the dependencies between the functions are regarded as the edges. Fault propagation tendency paths are discovered based on the function execution paths and minimum probability threshold. The frequency of each directed edge in the set of fault propagation tendency path is taken as the weight of the corresponding edge. Then some metrics related to ripple effects of probability are calculated. Finally, the nodes with the metric at top-k are taken as the important nodes. The experiment verifies the accuracy and efficiency of the algorithm MIN-REP.
AB - The complexity of software directly leads to an increasing cost in software testing and maintenance. Finding the important nodes with significant vulnerability is helpful for fault discovery and further reduces the damage to the software system. In this paper, a new algorithm named MIN-REP (Mining the Important Nodes based on Ripple Effects of Probability) is proposed to find out the paths with greater possibility for fault propagation, and then the important nodes are mined. To build a model of directed unweighted software network, functions are taken as the nodes and the dependencies between the functions are regarded as the edges. Fault propagation tendency paths are discovered based on the function execution paths and minimum probability threshold. The frequency of each directed edge in the set of fault propagation tendency path is taken as the weight of the corresponding edge. Then some metrics related to ripple effects of probability are calculated. Finally, the nodes with the metric at top-k are taken as the important nodes. The experiment verifies the accuracy and efficiency of the algorithm MIN-REP.
KW - Complex software network
KW - Fault propagation
KW - Important node
KW - Ripple effects of probability
UR - http://www.scopus.com/inward/record.url?scp=85072832096&partnerID=8YFLogxK
U2 - 10.1145/3321408.3322841
DO - 10.1145/3321408.3322841
M3 - Conference contribution
AN - SCOPUS:85072832096
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the ACM Turing Celebration Conference - China, ACM TURC 2019
PB - Association for Computing Machinery
T2 - 2019 ACM Turing Celebration Conference - China, ACM TURC 2019
Y2 - 17 May 2019 through 19 May 2019
ER -