Fast Deterministic Black-box Context-free Grammar Inference
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916093817257984 |
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| author | Arefin, Mohammad Rifat Shetiya, Suraj Wang, Zili Csallner, Christoph |
| author_facet | Arefin, Mohammad Rifat Shetiya, Suraj Wang, Zili Csallner, Christoph |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_06163 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fast Deterministic Black-box Context-free Grammar Inference Arefin, Mohammad Rifat Shetiya, Suraj Wang, Zili Csallner, Christoph Software Engineering Programming Languages 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). |
| title | Fast Deterministic Black-box Context-free Grammar Inference |
| topic | Software Engineering Programming Languages |
| url | https://arxiv.org/abs/2308.06163 |