The confidence in our k-Tails

Hila Cohen, Shahar Maoz

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

Abstract

K-Tails is a popular algorithm for extracting a candidate behavioral model from a log of execution traces. The usefulness of k-Tails depends on the quality of its input log, which may include too few traces to build a representative model, or too many traces, whose analysis is a waste of resources. Given a set of traces, how can one be confident that it includes enough, but not too many, traces? While many have used the k-Tails algorithm, no previous work has yet investigated this question. In this paper we address this question by proposing a novel notion of log completeness. Roughly, a log of traces, extracted from a given system, is k-complete, iff adding any new trace to the log will not change the resulting model k-Tails would build for it. Since the system and its full set of traces is unknown, we cannot know whether a given log is k-complete. However, we can estimate its k-completeness. We call this estimation k-confidence. We formalize the notion of k-confidence and implement its computation. Preliminary experiments show that k-confidence can be efficiently computed and is a highly reliable estimator for k-completeness.

Original languageEnglish
Title of host publicationASE 2014 - Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering
PublisherAssociation for Computing Machinery, Inc
Pages605-610
Number of pages6
ISBN (Electronic)9781450330138
DOIs
StatePublished - 2014
Event29th ACM/IEEE International Conference on Automated Software Engineering, ASE 2014 - Vasteras, Sweden
Duration: 15 Sep 201419 Sep 2014

Publication series

NameASE 2014 - Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

Conference

Conference29th ACM/IEEE International Conference on Automated Software Engineering, ASE 2014
Country/TerritorySweden
CityVasteras
Period15/09/1419/09/14

Keywords

  • Dynamic specification mining
  • Probabilistic approach

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