Learning the language of software errors
We propose to use algorithms for learning deterministic finite automata (DFA), such as Angluin’s L ∗ algorithm, for learning a DFA 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 functi...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
AI Access Foundation
2020
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| _version_ | 1826293031911817216 |
|---|---|
| author | Chockler, H Kesseli, P Kroenig, D Strichman, O |
| author_facet | Chockler, H Kesseli, P Kroenig, D Strichman, O |
| author_sort | Chockler, H |
| collection | OXFORD |
| description | We propose to use algorithms for learning deterministic finite automata (DFA), such
as Angluin’s L
∗ algorithm, for learning a DFA 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. It can also be used
for visually comparing different versions of a program (by presenting an automaton for the
behavior in the symmetric difference between them), and for assisting in merging several
development branches. We present experiments that demonstrate the power of an abstract
visual representation of errors and of program segments, accessible via the project’s web
page. In addition, our experiments in this paper demonstrate that such automata can
be learned efficiently over real-world programs. We also present lazy learning, which is a
method for reducing the number of membership queries while using L∗, and demonstrate its effectiveness on standard benchmarks. |
| first_indexed | 2024-03-07T03:23:50Z |
| format | Journal article |
| id | oxford-uuid:b859db0c-a3a3-496a-8160-1ea1a5fe21b5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:23:50Z |
| publishDate | 2020 |
| publisher | AI Access Foundation |
| record_format | dspace |
| spelling | oxford-uuid:b859db0c-a3a3-496a-8160-1ea1a5fe21b52022-03-27T04:55:17ZLearning the language of software errorsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b859db0c-a3a3-496a-8160-1ea1a5fe21b5EnglishSymplectic ElementsAI Access Foundation2020Chockler, HKesseli, PKroenig, DStrichman, OWe propose to use algorithms for learning deterministic finite automata (DFA), such as Angluin’s L ∗ algorithm, for learning a DFA 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. It can also be used for visually comparing different versions of a program (by presenting an automaton for the behavior in the symmetric difference between them), and for assisting in merging several development branches. We present experiments that demonstrate the power of an abstract visual representation of errors and of program segments, accessible via the project’s web page. In addition, our experiments in this paper demonstrate that such automata can be learned efficiently over real-world programs. We also present lazy learning, which is a method for reducing the number of membership queries while using L∗, and demonstrate its effectiveness on standard benchmarks. |
| spellingShingle | Chockler, H Kesseli, P Kroenig, D Strichman, O Learning the language of software errors |
| title | Learning the language of software errors |
| title_full | Learning the language of software errors |
| title_fullStr | Learning the language of software errors |
| title_full_unstemmed | Learning the language of software errors |
| title_short | Learning the language of software errors |
| title_sort | learning the language of software errors |
| work_keys_str_mv | AT chocklerh learningthelanguageofsoftwareerrors AT kesselip learningthelanguageofsoftwareerrors AT kroenigd learningthelanguageofsoftwareerrors AT strichmano learningthelanguageofsoftwareerrors |