Sound Non-Statistical Clustering of Static Analysis Alarms
- Title
- Sound Non-Statistical Clustering of Static Analysis Alarms
- Author
- 이우석
- Keywords
- Static analysis; abstract interpretation; false alarms
- Issue Date
- 2017-09
- Publisher
- ASSOC COMPUTING MACHINERY
- Citation
- ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS, v. 39, No. 4, Article no. 16
- 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.
- URI
- https://dl.acm.org/citation.cfm?id=3095021https://repository.hanyang.ac.kr/handle/20.500.11754/99246
- ISSN
- 0164-0925; 1558-4593
- DOI
- 10.1145/3095021
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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