Size and accuracy in model inference

Nimrod Busany, Shahar Maoz, Yehonatan Yulazari

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

Abstract

Many works infer finite-state models from execution logs. Large models are more accurate but also more difficult to present and understand. Small models are easier to present and understand but are less accurate. In this work we investigate the tradeoff between model size and accuracy in the context of the classic k-Tails model inference algorithm. First, we define mk-Tails, a generalization of k-Tails from one to many parameters, which enables fine-grained control over the tradeoff. Second, we extend mk-Tails with a reduction based on past-equivalence, which effectively reduces the size of the model without decreasing its accuracy. We implemented our work and evaluated its performance and effectiveness on real-world logs as well as on models and generated logs from the literature.

Original languageEnglish
Title of host publicationProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages887-898
Number of pages12
ISBN (Electronic)9781728125084
DOIs
StatePublished - Nov 2019
Event34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, United States
Duration: 10 Nov 201915 Nov 2019

Publication series

NameProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

Conference

Conference34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
Country/TerritoryUnited States
CitySan Diego
Period10/11/1915/11/19

Keywords

  • Log analysis
  • Model inference

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