Learning the Language of Error

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

doi:10.1007/978-3-319-24953-7_9

Learning the Language of Error

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

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

19 Citations (Scopus)

220 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Proceedings of ATVA 2015: Automated Technology for Verification and Analysis
Publisher	Springer
Pages	114-130
Number of pages	18
Volume	9364
DOIs	https://doi.org/10.1007/978-3-319-24953-7_9
Publication status	Published - Oct 2015

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag

Access to Document

10.1007/978-3-319-24953-7_9

Learning the Language of_CHAPMAN_Publishedonline22November2015_GREEN AAMAccepted author manuscript, 370 KB

Cite this

@inbook{a48749b2a4854d3db9555cf1a0d1232b,

title = "Learning the Language of Error",

abstract = "We propose to harness Angluin{\textquoteright}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.",

author = "Martin Chapman and Hana Chockler and Pascal Kesseli and Daniel Kroening and Ofer Strichman and Michael Tautschnig",

year = "2015",

month = oct,

doi = "10.1007/978-3-319-24953-7_9",

language = "English",

volume = "9364",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "114--130",

booktitle = "Proceedings of ATVA 2015: Automated Technology for Verification and Analysis",

}

TY - CHAP

T1 - Learning the Language of Error

AU - Chapman, Martin

AU - Chockler, Hana

AU - Kesseli, Pascal

AU - Kroening, Daniel

AU - Strichman, Ofer

AU - Tautschnig, Michael

PY - 2015/10

Y1 - 2015/10

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-24953-7_9

DO - 10.1007/978-3-319-24953-7_9

M3 - Conference paper

VL - 9364

T3 - Lecture Notes in Computer Science

SP - 114

EP - 130

BT - Proceedings of ATVA 2015: Automated Technology for Verification and Analysis

PB - Springer

ER -

Learning the Language of Error

Abstract

Publication series

Access to Document

Fingerprint

Cite this