Sound Non-Statistical Clustering of Static Analysis Alarms

• Autores: Woosuk Lee, Wonchan Lee, Dongok Kang, Kihong Heo, Hakjoo Oh, Kwangkeun Yi
• Localización: ACM transactions on programming languages and systems, ISSN 0164-0925, Vol. 39, Nº 4, 2017
• Idioma: inglés
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• Resumen

  • 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.