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Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning
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Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning
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Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning
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Paper
Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning
2021
Overview
Naming conventions are an important concern in large verification projects using proof assistants, such as Coq. In particular, lemma names are used by proof engineers to effectively understand and modify Coq code. However, providing accurate and informative lemma names is a complex task, which is currently often carried out manually. Even when lemma naming is automated using rule-based tools, generated names may fail to adhere to important conventions not specified explicitly. We demonstrate a toolchain, dubbed Roosterize, which automatically suggests lemma names in Coq projects. Roosterize leverages a neural network model trained on existing Coq code, thus avoiding manual specification of naming conventions. To allow proof engineers to conveniently access suggestions from Roosterize during Coq project development, we integrated the toolchain into the popular Visual Studio Code editor. Our evaluation shows that Roosterize substantially outperforms strong baselines for suggesting lemma names and is useful in practice. The demo video for Roosterize can be viewed at: https://youtu.be/HZ5ac7Q14rc.
Publisher
Cornell University Library, arXiv.org
Subject
