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 |
Keywords
- Abstract interpretation
- False alarms
- Static analysis
ASJC Scopus subject areas
- Software
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Lee, W., Lee, W., Kang, D., Heo, K., Oh, H., & Yi, K. (2017). Sound non-statistical clustering of static analysis alarms. ACM Transactions on Programming Languages and Systems, 39(4), [16]. https://doi.org/10.1145/3095021