The confidence in our k-tails

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.

BibTeX

@inproceedings{Cohen-Maoz:ASE14,
  author    = {Hila Cohen and
               Shahar Maoz},
  title     = {The confidence in our k-tails},
  booktitle = {ASE},
  pages     = {605--610},
  publisher = {{ACM}},
  year      = {2014},
}

The confidence in our k-tails

Abstract

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