Sound Non-Statistical Clustering of Static Analysis Alarms

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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.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2017-09
Language
English
Article Type
Article
Citation

ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS, v.39, no.4

ISSN
0164-0925
DOI
10.1145/3095021
URI
http://hdl.handle.net/10203/271734
Appears in Collection
CS-Journal Papers(저널논문)
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