Affiliation:
1. Seoul National University, Seoul, Korea
2. Stanford University
3. Korea University, Seongbuk-gu, Seoul, Korea
Abstract
We present a sound method for clustering alarms from static analyzers. Our method clusters alarms by discovering sound dependencies between them such that if the dominant alarms of a cluster turns out to be false, all the other alarms in the same cluster are guaranteed to be false. We have implemented our clustering algorithm on top of a realistic buffer-overflow analyzer and proved that our method reduces 45% of alarm reports. Our framework is applicable to any abstract interpretation-based static analysis and orthogonal to abstraction refinements and statistical ranking schemes.
Funder
Engineering Research Center of Excellence Program of Korea Ministry of Science, ICT 8 Future Planning( MSIP) / National Research Foundation of Kore
Development of Vulnerability Discovery Technologies for IoT Software Security
Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT 8 Future Planning
Institute for Information 8 communications Technology Promotion (IITP) grant funded by the Korea government
Samsung Electronics Software Center
Publisher
Association for Computing Machinery (ACM)
Cited by
13 articles.
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