Sound Non-Statistical Clustering of Static Analysis Alarms
- Authors
- Lee, Woosuk; Lee, Wonchan; Kang, Dongok; Heo, Kihong; Oh, Hakjoo; Yi, Kwangkeun
- Issue Date
- Sep-2017
- Publisher
- ASSOC COMPUTING MACHINERY
- Keywords
- Static analysis; abstract interpretation; false alarms
- Citation
- ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS, v.39, no.4, pp.1 - 35
- Indexed
- SCIE
SCOPUS
- Journal Title
- ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS
- Volume
- 39
- Number
- 4
- Start Page
- 1
- End Page
- 35
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/9049
- DOI
- 10.1145/3095021
- ISSN
- 0164-0925
- 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.
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