FMitF: Collaborative Research: Track I: Finding and Eliminating Bugs in Operating Systems
FMitF: Collaborative Research: Track I: Finding and Eliminating Bugs in Operating Systems
批准号:
1918056
负责人:
Dawson Engler
金额:
$25.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-07-01 至 2023-06-30
中文摘要
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英文摘要
Operating systems are both huge -- tens of millions of lines of code -- and hugely important -- they manage resources and provide services to the applications that run society. Like all software, operating systems contain bugs. Unfortunately, bugs in such foundational systems can have catastrophic consequences, from large-scale data leaks to complete machine takeovers by malicious agents. The team of researchers develops new tools that can be used to find and eliminate such bugs before a system is deployed, when the bugs can compromise performance, reliability, and security. The project's novelties are foundational techniques, languages, and algorithms that empower software developers to describe buggy patterns that allow automated tools to scale and find bugs in many millions of lines of code. The project's impacts will be in improving the robustness, reliability, and security of real-world operating systems. Existing approaches to bug finding either are precise or scale to large systems but not both. This project reconciles scalability and precision with a key insight: that system-specific extensibility will allow developers to extend core algorithms to check for properties and patterns that are important to their particular systems in a way that scales to large code bases. To this end, the researchers develop new symbolic-execution-based methods that are extensible, precise and scalable, thereby allowing developers to easily customize extensions to focus on likely error patterns while allowing them to swiftly ignore many millions of lines of irrelevant code. The speed, precision and scalability in turn allows developers to directly integrate the project's tools into their software-development cycle to eliminate bugs well before deployment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1109/sp46214.2022.9833782
发表时间:
2022-05
期刊:
2022 IEEE Symposium on Security and Privacy (SP)
影响因子:
--
作者:
[Alex Ozdemir;Fraser Brown;R. Wahby]
通讯作者:
Alex Ozdemir;Fraser Brown;R. Wahby
CSR: Large: Collaborative Research: SemGrep: a System for Improving Software Reliability Through Semantic Similarity Bug Search
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批准号:1012107
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项目类别:Standard Grant
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资助金额:$13.1万
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财政年份:2010
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负责人:Dawson Engler
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依托单位:
CAREER: Effective Methods for Finding Software Errors
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批准号:0238570
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项目类别:Continuing Grant
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资助金额:$40.0万
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财政年份:2003
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负责人:Dawson Engler
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依托单位:
海外基金