Chasing Errors Using Biasing Automata

  • Lei Bu
  • Doron PeledEmail author
  • Dashuan Shen
  • Yael Tzirulnikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11245)


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.



The second author would like to thank Sergiy Bogomolov and Ken McMillan for interesting discussions on this subject.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lei Bu
    • 2
  • Doron Peled
    • 1
    Email author
  • Dashuan Shen
    • 2
  • Yael Tzirulnikov
    • 1
  1. 1.Department of Computer ScienceBar Ilan UniversityRamat GanIsrael
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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