Learning the Language of Error

Martin Chapman, Hana Chockler, Pascal Kesseli, Daniel Kroening, Ofer Strichman, Michael Tautschnig

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

17 Citations (Scopus)
184 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of ATVA 2015: Automated Technology for Verification and Analysis
Number of pages18
Publication statusPublished - Oct 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag


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