Learning the Language of Error

  • Martin Chapman
  • Hana ChocklerEmail author
  • Pascal Kesseli
  • Daniel Kroening
  • Ofer Strichman
  • Michael Tautschnig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9364)


We propose to harness Angluin’s \(L^*\) algorithm for learning a deterministic finite automaton that describes the possible scenarios under which a given program error occurs. The alphabet of this automaton is given by the user (for instance, a subset of the function call sites or branches), and hence the automaton describes a user-defined abstraction of those scenarios. More generally, the same technique can be used for visualising the behavior of a program or parts thereof. This can be used, for example, for visually comparing different versions of a program, by presenting an automaton for the behavior in the symmetric difference between them, or for assisting in merging several development branches. We present initial experiments that demonstrate the power of an abstract visual representation of errors and of program segments.


Word Length Regular Language Membership Query Deterministic Finite Automaton Error Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Martin Chapman
    • 1
  • Hana Chockler
    • 1
    Email author
  • Pascal Kesseli
    • 2
  • Daniel Kroening
    • 2
  • Ofer Strichman
    • 3
  • Michael Tautschnig
    • 4
  1. 1.Department of InformaticsKing’s College LondonLondonUK
  2. 2.Department of Computer ScienceUniversity of OxfordOxfordUK
  3. 3.Information Systems EngineeringTechnionHaifaIsrael
  4. 4.EECSQueen Mary University of LondonLondonUK

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