Fast Deterministic Black-box Context-free Grammar Inference.
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| Title: | Fast Deterministic Black-box Context-free Grammar Inference. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| DOI: | 10.1145/3597503.3639214 |
