Tehnički vjesnik, Vol. 22 No. 2, 2015.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20150317102804
Research of alarm correlations based on static defect detection
Dalin Zhang
orcid.org/0000-0003-0346-7020
; National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing, China
Dahai Jin
; State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing, China
Yunzhan Gong
; State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing, China
Siru Chen
; History and Tourism Management, Inner Mongolia University, Hohhot, China
Chengcheng Wang
; Shanghai Stock Exchange, Shanghai, China
Sažetak
Traditional static defect detection tools can detect software defects and report alarms, but the correlations among alarms are not identified and massive independent alarms are against the understanding. Helping users in the alarm verification task is a major challenge for current static defect detection tools. In this paper, we formally introduce alarm correlations. If the occurrence of one alarm causes another alarm, we say that they are correlated. If one dominant alarm is uniquely correlated with another, we know verifying the first will also verify the others. Guided by the correlation, we can reduce the number of alarms required for verification. Our algorithms are inter-procedural, path-sensitive, and scalable. We present a correlation procedure summary model for inter-procedural alarm correlation calculation. The underlying algorithms are implemented inside our defect detection tools. We chose one common semantic fault as a case study and proved that our method has the effect of reducing 34,23 % of workload. Using correlation information, we are able to automate the alarm verification that previously had to be done manually.
Ključne riječi
abstract interpretation; alarm correlations; alarm verification; correlation summary; state slicing
Hrčak ID:
138082
URI
Datum izdavanja:
22.4.2015.
Posjeta: 1.978 *