Sound non-statistical clustering of static analysis alarms

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Article number16
JournalACM Transactions on Programming Languages and Systems
Volume39
Issue number4
DOIs
Publication statusPublished - 2017 Aug 1

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Static analysis
Acoustic waves
Clustering algorithms

Keywords

  • Abstract interpretation
  • False alarms
  • Static analysis

ASJC Scopus subject areas

  • Software

Cite this

Sound non-statistical clustering of static analysis alarms. / Lee, Woosuk; Lee, Wonchan; Kang, Dongok; Heo, Kihong; Oh, Hakjoo; Yi, Kwangkeun.

In: ACM Transactions on Programming Languages and Systems, Vol. 39, No. 4, 16, 01.08.2017.

Research output: Contribution to journalArticle

Lee, Woosuk ; Lee, Wonchan ; Kang, Dongok ; Heo, Kihong ; Oh, Hakjoo ; Yi, Kwangkeun. / Sound non-statistical clustering of static analysis alarms. In: ACM Transactions on Programming Languages and Systems. 2017 ; Vol. 39, No. 4.
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