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 language | English |
|---|---|
| Article number | 16 |
| Journal | ACM Transactions on Programming Languages and Systems |
| Volume | 39 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2017 Aug 1 |
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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 journal › Article
}
TY - JOUR
T1 - Sound non-statistical clustering of static analysis alarms
AU - Lee, Woosuk
AU - Lee, Wonchan
AU - Kang, Dongok
AU - Heo, Kihong
AU - Oh, Hakjoo
AU - Yi, Kwangkeun
PY - 2017/8/1
Y1 - 2017/8/1
N2 - 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.
AB - 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.
KW - Abstract interpretation
KW - False alarms
KW - Static analysis
UR - http://www.scopus.com/inward/record.url?scp=85028504442&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028504442&partnerID=8YFLogxK
U2 - 10.1145/3095021
DO - 10.1145/3095021
M3 - Article
AN - SCOPUS:85028504442
VL - 39
JO - ACM Transactions on Programming Languages and Systems
JF - ACM Transactions on Programming Languages and Systems
SN - 0164-0925
IS - 4
M1 - 16
ER -