ICoq:大规模验证项目的回归证明选择

Ahmet Çelik, Karl Palmskog, Miloš Gligorić
{"title":"ICoq:大规模验证项目的回归证明选择","authors":"Ahmet Çelik, Karl Palmskog, Miloš Gligorić","doi":"10.1109/ASE.2017.8115630","DOIUrl":null,"url":null,"abstract":"Proof assistants such as Coq are used to construct and check formal proofs in many large-scale verification projects. As proofs grow in number and size, the need for tool support to quickly find failing proofs after revising a project increases. We present a technique for large-scale regression proof selection, suitable for use in continuous integration services, e.g., Travis CI. We instantiate the technique in a tool dubbed iCoq. iCoq tracks fine-grained dependencies between Coq definitions, propositions, and proofs, and only checks those proofs affected by changes between two revisions. iCoq additionally saves time by ignoring changes with no impact on semantics. We applied iCoq to track dependencies across many revisions in several large Coq projects and measured the time savings compared to proof checking from scratch and when using Coq's timestamp-based toolchain for incremental checking. Our results show that proof checking with iC oq is up to 10 times faster than the former and up to 3 times faster than the latter.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"ICoq: Regression proof selection for large-scale verification projects\",\"authors\":\"Ahmet Çelik, Karl Palmskog, Miloš Gligorić\",\"doi\":\"10.1109/ASE.2017.8115630\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proof assistants such as Coq are used to construct and check formal proofs in many large-scale verification projects. As proofs grow in number and size, the need for tool support to quickly find failing proofs after revising a project increases. We present a technique for large-scale regression proof selection, suitable for use in continuous integration services, e.g., Travis CI. We instantiate the technique in a tool dubbed iCoq. iCoq tracks fine-grained dependencies between Coq definitions, propositions, and proofs, and only checks those proofs affected by changes between two revisions. iCoq additionally saves time by ignoring changes with no impact on semantics. We applied iCoq to track dependencies across many revisions in several large Coq projects and measured the time savings compared to proof checking from scratch and when using Coq's timestamp-based toolchain for incremental checking. Our results show that proof checking with iC oq is up to 10 times faster than the former and up to 3 times faster than the latter.\",\"PeriodicalId\":382876,\"journal\":{\"name\":\"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASE.2017.8115630\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASE.2017.8115630","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

在许多大型验证项目中,Coq等证明助手被用于构造和检查形式证明。随着证明在数量和大小上的增长,对工具支持的需求增加,以便在修改项目后快速找到失败的证明。我们提出了一种大规模回归证明选择技术,适用于持续集成服务,例如Travis CI。我们在一个名为iCoq的工具中实例化了该技术。iCoq跟踪Coq定义、命题和证明之间的细粒度依赖关系,并且只检查那些受两次修订之间更改影响的证明。iCoq还通过忽略不影响语义的更改来节省时间。我们在几个大型Coq项目中应用iCoq来跟踪许多修订版之间的依赖关系,并测量了与从头开始进行证明检查和使用Coq的基于时间戳的工具链进行增量检查相比节省的时间。我们的结果表明,使用iC oq的证明检查速度比前者快10倍,比后者快3倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ICoq: Regression proof selection for large-scale verification projects
Proof assistants such as Coq are used to construct and check formal proofs in many large-scale verification projects. As proofs grow in number and size, the need for tool support to quickly find failing proofs after revising a project increases. We present a technique for large-scale regression proof selection, suitable for use in continuous integration services, e.g., Travis CI. We instantiate the technique in a tool dubbed iCoq. iCoq tracks fine-grained dependencies between Coq definitions, propositions, and proofs, and only checks those proofs affected by changes between two revisions. iCoq additionally saves time by ignoring changes with no impact on semantics. We applied iCoq to track dependencies across many revisions in several large Coq projects and measured the time savings compared to proof checking from scratch and when using Coq's timestamp-based toolchain for incremental checking. Our results show that proof checking with iC oq is up to 10 times faster than the former and up to 3 times faster than the latter.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
TiQi: A natural language interface for querying software project data A comprehensive study on real world concurrency bugs in Node.js Managing software evolution through semantic history slicing Software performance self-adaptation through efficient model predictive control Privacy-aware data-intensive applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1