Behavioral log analysis with statistical guarantees

Nimrod Busany, Shahar Maoz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2015 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2015 - Proceedings
PublisherAssociation for Computing Machinery, Inc
Pages898-901
Number of pages4
ISBN (Electronic)9781450336758
DOIs
StatePublished - 30 Aug 2015
Event10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2015 - Bergamo, Italy
Duration: 30 Aug 20154 Sep 2015

Publication series

Name2015 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2015 - Proceedings

Conference

Conference10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2015
Country/TerritoryItaly
CityBergamo
Period30/08/154/09/15

Keywords

  • Log analysis
  • Specification mining

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