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ROOSTERIZE: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning
Univ Texas Austin, Austin, TX 78712 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS.ORCID iD: 0000-0003-0228-1240
Univ Texas Austin, Austin, TX 78712 USA..
Univ Texas Austin, Austin, TX 78712 USA..
2021 (English)In: 2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2021), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 21-24Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 21-24
Series
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Keywords [en]
Coq, lemma names, neural networks
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-304562DOI: 10.1109/ICSE-Companion52605.2021.00026ISI: 000706450400006Scopus ID: 2-s2.0-85115700795OAI: oai:DiVA.org:kth-304562DiVA, id: diva2:1609396
Conference
IEEE/ACM 43rd International Conference on Software Engineering (ICSE), MAY 25-28, 2021, ELECTR NETWORK
Note

Part of proceedings: ISBN 978-1-6654-1219-3, QC 20230117

Available from: 2021-11-08 Created: 2021-11-08 Last updated: 2023-01-17Bibliographically approved

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Palmskog, Karl

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CiteExportLink to record
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  • apa
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