Behavioral log analysis with statistical guarantees

Abstract

Scalability is a major challenge for existing behavioral log analysis algorithms, which extract finite-state automaton models or temporal properties from logs generated by running systems. In this work we propose to address scalability using statistical tools. The key to our approach is to consider behavioral log analysis as a statistical experiment. Rather than analyzing the entire log, we suggest to analyze only a sample of traces from the log and, most importantly, provide means to compute statistical guarantees for the correctness of the analysis result. We present two example applications of our approach as well as initial evidence for its effectiveness.