TY - GEN
T1 - Use Neural Network to Improve Fault Injection Testing
AU - Wang, Yichen
AU - Wang, Yikun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/7
Y1 - 2017/8/7
N2 - fault injection is an effective technique in software testing. By introducing faults to software under test, fault injection can improve the coverage of a test, as the same time, the fault injected in software contributes significantly to find true fault related to fault injected. In this paper we propose a software testing method based on fault injection. In this method, we first use neural network to calculate the value of fitness function that describe the proximity of two paths, and then use simulated annealing algorithm to generate test data. In this method we use the value of fitness function as the criteria in simulated annealing algorithm. Experiment shows that this method can improve the efficiency of generating test data obviously.
AB - fault injection is an effective technique in software testing. By introducing faults to software under test, fault injection can improve the coverage of a test, as the same time, the fault injected in software contributes significantly to find true fault related to fault injected. In this paper we propose a software testing method based on fault injection. In this method, we first use neural network to calculate the value of fitness function that describe the proximity of two paths, and then use simulated annealing algorithm to generate test data. In this method we use the value of fitness function as the criteria in simulated annealing algorithm. Experiment shows that this method can improve the efficiency of generating test data obviously.
KW - fault propagation path
KW - neural network
KW - simulated annealing algorithm
KW - software fault injection
KW - software testing
UR - https://www.scopus.com/pages/publications/85034416308
U2 - 10.1109/QRS-C.2017.69
DO - 10.1109/QRS-C.2017.69
M3 - 会议稿件
AN - SCOPUS:85034416308
T3 - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
SP - 377
EP - 384
BT - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
Y2 - 25 July 2017 through 29 July 2017
ER -