Sound non-statistical clustering of static analysis alarms

Woosuk Lee, Wonchan Lee, Dongok Kang, Kihong Heo, Hakjoo Oh, Kwangkeun Yi

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Article number16
JournalACM Transactions on Programming Languages and Systems
Issue number4
Publication statusPublished - 2017 Aug

Bibliographical note

Publisher Copyright:
© 2017 ACM.


  • Abstract interpretation
  • False alarms
  • Static analysis

ASJC Scopus subject areas

  • Software


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