Visibly Pushdown Grammar Inference from Program Inputs

Konsep Inti

V-Star is a novel grammar inference tool that learns Visibly Pushdown Grammars (VPGs) from program inputs without prior knowledge of the nesting structure.

Abstrak

The paper introduces V-Star, a novel grammar inference framework designed to learn Visibly Pushdown Grammars (VPGs) from a black-box program using a collection of sample seed strings.
The key highlights and insights are:

V-Star adapts Angluin's L-Star algorithm to learn VPGs, integrating novel techniques such as nesting patterns to infer call and return tokens without prior knowledge.

The authors provide a theoretical analysis showing that V-Star can achieve exact learning of VPGs under certain conditions, even when the tagging function (call/return symbols) is not known a priori.

The evaluation demonstrates that V-Star outperforms state-of-the-art grammar learning tools in accurately learning practical grammars like XML, JSON, and S-Expressions. This highlights the benefits of utilizing nesting structures and V-Star's ability to simulate equivalence queries through sampling.

V-Star addresses limitations of prior VPG learning approaches that assume known nesting patterns or character-level tagging, making it more practical for real-world applications.

Statistik

Accurate description of program inputs remains a critical challenge in the field of programming languages.
Active learning, as a well-established field, achieves exact learning for regular languages.
VPGs formally specify nesting structures and can describe many practical format languages such as XML and JSON.
Learning VPGs is a problem that fits nicely into the well-studied active learning field.

Kutipan

"V-Star is a novel tool for VPG inference. Its algorithm adapts Angluin's L-Star algorithm and integrates a set of novel techniques such as nesting patterns to infer call and return tokens."
"We provide a theoretical analysis elucidating the conditions under which V-Star achieves accurate learning."
"Our evaluation of V-Star demonstrates its better accuracy in learning practical grammars, in comparison with state-of-the-art grammar-learning tools."

Wawasan Utama Disaring Dari

V-Star

by Xiaodong Jia... pada arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04201.pdf

Pertanyaan yang Lebih Dalam

How can V-Star's techniques be extended to learn more expressive grammar formalisms beyond VPGs

V-Star's techniques can be extended to learn more expressive grammar formalisms beyond VPGs by incorporating additional features and complexities into the learning process. One way to achieve this is by introducing more sophisticated algorithms for handling nested structures and more intricate grammar rules. By enhancing the tagging inference mechanism to handle a wider range of symbols and patterns, V-Star can adapt to the complexities of languages with more intricate nesting structures. Additionally, incorporating advanced techniques from formal language theory and automata theory can help V-Star tackle more expressive grammar formalisms such as context-sensitive grammars or even Turing machines. By expanding the capabilities of V-Star to handle more complex grammar formalisms, the tool can be applied to a broader range of languages and computational models.

What are the limitations of V-Star's approach, and how can it be further improved to handle a wider range of practical languages

The limitations of V-Star's approach lie in its reliance on a predefined tagging function and the assumption of unique pairing of call and return symbols in the oracle language. To improve the tool's effectiveness and applicability, these limitations can be addressed by developing more robust algorithms for inferring tagging functions dynamically from sample inputs. By enhancing the algorithm to handle non-unique pairings of symbols and more complex nesting structures, V-Star can improve its accuracy and efficiency in learning a wider range of practical languages. Additionally, incorporating techniques for handling ambiguous grammars and non-deterministic structures can further enhance V-Star's capabilities. By addressing these limitations and improving the tool's adaptability to diverse language structures, V-Star can become more versatile and effective in grammar inference tasks.

What other applications beyond program analysis could benefit from V-Star's grammar inference capabilities, and how could the tool be adapted to those domains

Beyond program analysis, V-Star's grammar inference capabilities can benefit various domains such as natural language processing, data processing, and information retrieval. In natural language processing, V-Star can be adapted to learn the grammar of human languages, enabling applications in machine translation, sentiment analysis, and text generation. In data processing, V-Star can be used to infer the structure of data formats, facilitating tasks like data validation, transformation, and integration. In information retrieval, V-Star can aid in parsing and understanding unstructured data sources, improving search accuracy and relevance. To adapt V-Star to these domains, the tool can be customized to handle specific language structures and grammar rules prevalent in each domain, enhancing its applicability and utility in diverse applications.