Learning the language of error

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

doi:https://doi.org/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

Data and Decision Sciences

Technion - Israel Institute of Technology

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

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	Automated Technology for Verification and Analysis - 13th International Symposium, ATVA 2015, Proceedings
Editors	Bernd Finkbeiner, Geguang Pu, Lijun Zhang
Pages	114-130
Number of pages	17
DOIs	https://doi.org/10.1007/978-3-319-24953-7_9
State	Published - 2015
Event	13th International Symposium on Automated Technology for Verification and Analysis, ATVA 2015 - Shanghai, China Duration: 12 Oct 2015 → 15 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9364

Conference

Conference	13th International Symposium on Automated Technology for Verification and Analysis, ATVA 2015
Country/Territory	China
City	Shanghai
Period	12/10/15 → 15/10/15

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

https://doi.org/10.1007/978-3-319-24953-7_9

Cite this

Chapman, M., Chockler, H., Kesseli, P., Kroening, D., Strichman, O., & Tautschnig, M. (2015). Learning the language of error. In B. Finkbeiner, G. Pu, & L. Zhang (Eds.), Automated Technology for Verification and Analysis - 13th International Symposium, ATVA 2015, Proceedings (pp. 114-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9364). https://doi.org/10.1007/978-3-319-24953-7_9

Chapman, Martin ; Chockler, Hana ; Kesseli, Pascal et al. / Learning the language of error. Automated Technology for Verification and Analysis - 13th International Symposium, ATVA 2015, Proceedings. editor / Bernd Finkbeiner ; Geguang Pu ; Lijun Zhang. 2015. pp. 114-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4b33b68a95514161bc96528100acc114,

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",

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Chapman, M, Chockler, H, Kesseli, P, Kroening, D, Strichman, O & Tautschnig, M 2015, Learning the language of error. in B Finkbeiner, G Pu & L Zhang (eds), Automated Technology for Verification and Analysis - 13th International Symposium, ATVA 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9364, pp. 114-130, 13th International Symposium on Automated Technology for Verification and Analysis, ATVA 2015, Shanghai, China, 12/10/15. https://doi.org/10.1007/978-3-319-24953-7_9

Learning the language of error. / Chapman, Martin; Chockler, Hana; Kesseli, Pascal et al.
Automated Technology for Verification and Analysis - 13th International Symposium, ATVA 2015, Proceedings. ed. / Bernd Finkbeiner; Geguang Pu; Lijun Zhang. 2015. p. 114-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9364).

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

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T1 - Learning the language of error

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AU - Chockler, Hana

AU - Kesseli, Pascal

AU - Kroening, Daniel

AU - Strichman, Ofer

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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.

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M3 - منشور من مؤتمر

SN - 9783319249520

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Automated Technology for Verification and Analysis - 13th International Symposium, ATVA 2015, Proceedings

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Chapman M, Chockler H, Kesseli P, Kroening D, Strichman O, Tautschnig M. Learning the language of error. In Finkbeiner B, Pu G, Zhang L, editors, Automated Technology for Verification and Analysis - 13th International Symposium, ATVA 2015, Proceedings. 2015. p. 114-130. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-319-24953-7_9

Learning the language of error

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