NerdBug: automated bug detection in neural networks

Foad Jafarinejad, Krishna Narasimhan, M. Mezini
{"title":"NerdBug: automated bug detection in neural networks","authors":"Foad Jafarinejad, Krishna Narasimhan, M. Mezini","doi":"10.1145/3464968.3468409","DOIUrl":null,"url":null,"abstract":"Despite the exponential growth of deep learning software during the last decade, there is a lack of tools to test and debug issues in deep learning programs. Current static analysis tools do not address challenges specific to deep learning as observed by past research on bugs specific to this area. Existing deep learning bug detection tools focus on specific issues like shape mismatches. In this paper, we present a vision for an abstraction-based approach to detect deep learning bugs and the plan to evaluate our approach. The motivation behind the abstraction-based approach is to be able to build an intermediate version of the neural network that can be analyzed in development time to provide live feedback programmers are used to with other kind of bugs.","PeriodicalId":295937,"journal":{"name":"Proceedings of the 1st ACM International Workshop on AI and Software Testing/Analysis","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st ACM International Workshop on AI and Software Testing/Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3464968.3468409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Despite the exponential growth of deep learning software during the last decade, there is a lack of tools to test and debug issues in deep learning programs. Current static analysis tools do not address challenges specific to deep learning as observed by past research on bugs specific to this area. Existing deep learning bug detection tools focus on specific issues like shape mismatches. In this paper, we present a vision for an abstraction-based approach to detect deep learning bugs and the plan to evaluate our approach. The motivation behind the abstraction-based approach is to be able to build an intermediate version of the neural network that can be analyzed in development time to provide live feedback programmers are used to with other kind of bugs.
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
NerdBug:神经网络中的自动错误检测
尽管深度学习软件在过去十年中呈指数级增长,但深度学习程序中缺乏测试和调试问题的工具。目前的静态分析工具并没有解决深度学习特有的挑战,正如过去对该领域特定bug的研究所观察到的那样。现有的深度学习漏洞检测工具专注于形状不匹配等特定问题。在本文中,我们提出了一种基于抽象的方法来检测深度学习错误,并计划评估我们的方法。基于抽象的方法背后的动机是能够构建神经网络的中间版本,可以在开发时进行分析,以提供实时反馈,程序员习惯于处理其他类型的错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
NerdBug: automated bug detection in neural networks Automated cell header generator for Jupyter notebooks On the use of evolutionary algorithms for test case prioritization in regression testing considering requirements dependencies Impact of programming languages on machine learning bugs
×
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