Chasing errors using biasing automata

Lei Bu, Doron Peled, Dashuan Shen, Yael Tzirulnikov

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

Abstract

Randomized testing is a lightweight approach for searching for bugs. It presents a tradeoff between the number of testing experiments performed and the probability to find errors. An important challenge in random testing is when the errors that we try to detect are scattered with very low probability among the different executions, forming a “rare event”. We suggest here the use of a “biasing automaton”, which observes the tested sequence and controls the distribution of the different choices of extending it. By the careful selection of a biasing automaton, we can increase the chance of errors to be found and consequently reduce the number of tests we need to perform. The biasing automaton is constructed through repeated testing of variants of the system under test. We show how to construct biasing automata based on genetic programming.

Original languageEnglish
Title of host publicationLeveraging Applications of Formal Methods, Verification and Validation. Verification - 8th International Symposium, ISoLA 2018, Proceedings
EditorsTiziana Margaria, Bernhard Steffen
PublisherSpringer Verlag
Pages271-286
Number of pages16
ISBN (Print)9783030034207
DOIs
StatePublished - 2018
Event8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018 - Limassol, Cyprus
Duration: 5 Nov 20189 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11245 LNCS

Conference

Conference8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018
Country/TerritoryCyprus
CityLimassol
Period5/11/189/11/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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