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Fast Deterministic Black-box Context-free Grammar Inference.

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Title: Fast Deterministic Black-box Context-free Grammar Inference.
Authors: Arefin, Mohammad Rifat1 mxa7262@mavs.uta.edu, Shetiya, Suraj1 suraj.shetiya@mavs.uta.edu, Wang, Zili2 ziliw1@iastate.edu, Csallner, Christoph1 csallner@uta.edu
Source: ICSE: International Conference on Software Engineering. 2024, p1-12. 12p.
Subjects: Generalization, Heuristic programming, Source code, Open source software, Deterministic algorithms
Abstract: Black-box context-free grammar inference is a hard problem as in many practical settings it only has access to a limited number of example programs. The state-of-the-art approach Arvada heuristically generalizes grammar rules starting from flat parse trees and is non-deterministic to explore different generalization sequences. We observe that many of Arvada's generalization steps violate common language concept nesting rules. We thus propose to pre-structure input programs along these nesting rules, apply learnt rules recursively, and make black-box context-free grammar inference deterministic. The resulting TreeVada yielded faster runtime and higher-quality grammars in an empirical comparison. The TreeVada source code, scripts, evaluation parameters, and training data are open-source and publicly available (https://doi.org/10.6084/m9.figshare.23907738). [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Arefin%2C+Mohammad+Rifat%22">Arefin, Mohammad Rifat</searchLink><relatesTo>1</relatesTo><i> mxa7262@mavs.uta.edu</i><br /><searchLink fieldCode="AR" term="%22Shetiya%2C+Suraj%22">Shetiya, Suraj</searchLink><relatesTo>1</relatesTo><i> suraj.shetiya@mavs.uta.edu</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Zili%22">Wang, Zili</searchLink><relatesTo>2</relatesTo><i> ziliw1@iastate.edu</i><br /><searchLink fieldCode="AR" term="%22Csallner%2C+Christoph%22">Csallner, Christoph</searchLink><relatesTo>1</relatesTo><i> csallner@uta.edu</i>
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  Data: Black-box context-free grammar inference is a hard problem as in many practical settings it only has access to a limited number of example programs. The state-of-the-art approach Arvada heuristically generalizes grammar rules starting from flat parse trees and is non-deterministic to explore different generalization sequences. We observe that many of Arvada's generalization steps violate common language concept nesting rules. We thus propose to pre-structure input programs along these nesting rules, apply learnt rules recursively, and make black-box context-free grammar inference deterministic. The resulting TreeVada yielded faster runtime and higher-quality grammars in an empirical comparison. The TreeVada source code, scripts, evaluation parameters, and training data are open-source and publicly available (https://doi.org/10.6084/m9.figshare.23907738). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1145/3597503.3639214
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      – Code: eng
        Text: English
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        PageCount: 12
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        Type: general
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        Type: general
      – SubjectFull: Source code
        Type: general
      – SubjectFull: Open source software
        Type: general
      – SubjectFull: Deterministic algorithms
        Type: general
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      – TitleFull: Fast Deterministic Black-box Context-free Grammar Inference.
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            NameFull: Wang, Zili
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          Dates:
            – D: 01
              M: 05
              Text: 2024
              Type: published
              Y: 2024
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            – TitleFull: ICSE: International Conference on Software Engineering
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