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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.04282 |
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| _version_ | 1866910437064310784 |
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| author | Carrott, Pedro Saavedra, Nuno Thompson, Kyle Lerner, Sorin Ferreira, João F. First, Emily |
| author_facet | Carrott, Pedro Saavedra, Nuno Thompson, Kyle Lerner, Sorin Ferreira, João F. First, Emily |
| contents | Proof assistants enable users to develop machine-checked proofs regarding software-related properties. Unfortunately, the interactive nature of these proof assistants imposes most of the proof burden on the user, making formal verification a complex, and time-consuming endeavor. Recent automation techniques based on neural methods address this issue, but require good programmatic support for collecting data and interacting with proof assistants. This paper presents CoqPyt, a Python tool for interacting with the Coq proof assistant. CoqPyt improves on other Coq-related tools by providing novel features, such as the extraction of rich premise data. We expect our work to aid development of tools and techniques, especially LLM-based, designed for proof synthesis and repair. A video describing and demonstrating CoqPyt is available at: https://youtu.be/fk74o0rePM8. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04282 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoqPyt: Proof Navigation in Python in the Era of LLMs Carrott, Pedro Saavedra, Nuno Thompson, Kyle Lerner, Sorin Ferreira, João F. First, Emily Software Engineering Proof assistants enable users to develop machine-checked proofs regarding software-related properties. Unfortunately, the interactive nature of these proof assistants imposes most of the proof burden on the user, making formal verification a complex, and time-consuming endeavor. Recent automation techniques based on neural methods address this issue, but require good programmatic support for collecting data and interacting with proof assistants. This paper presents CoqPyt, a Python tool for interacting with the Coq proof assistant. CoqPyt improves on other Coq-related tools by providing novel features, such as the extraction of rich premise data. We expect our work to aid development of tools and techniques, especially LLM-based, designed for proof synthesis and repair. A video describing and demonstrating CoqPyt is available at: https://youtu.be/fk74o0rePM8. |
| title | CoqPyt: Proof Navigation in Python in the Era of LLMs |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2405.04282 |