CRII: SHF: Automatic Extraction of Error-Handling Specifications in Systems Software
CRII: SHF: Automatic Extraction of Error-Handling Specifications in Systems Software
批准号:
1464439
负责人:
Cindy Rubio Gonzalez
金额:
$17.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-03-15 至 2018-09-30
中文摘要
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英文摘要
Software error handling is the process of detecting and responding tothe occurrence of errors during the execution of a program. Ideally,whenever a runtime error occurs, software systems should respondaccording to the programmer?s intent. Unfortunately, that is often notthe case. Error-handling code is difficult to write, and tends to bepoorly understood, poorly documented, and poorly tested.Unsurprisingly, error-handling code is often buggy. Bugs in softwareerror handlers are some of the most pervasive, dangerous, anddifficult to detect bugs. Incorrect error-handling is particularlyalarming in systems software (e.g., the operating system) because userapplications depend on the reliability of systems software. Thisresearch aims to gather a better understanding of error handling insystems software through automatic inference of error-handlingspecifications.Error-handling specifications describe how the system detects andrecovers from errors. This research applies static program analysistechniques to automatically infer error-handling specifications insystems software. This task is particularly challenging becausesystems software implements numerous failure policies, anderror-handling code is often diffused through the system. Furthermore,the analysis of such large code bases often faces scalabilityproblems. Understanding existing error-handling strategies is thefirst step to ensure systems software is reliable. This understandingalso has the potential to lead to the development of newerror-handling mechanisms, and new programming language support forerror handling. Both of these could have a significant impact onsoftware reliability beyond systems software.
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Collaborative Research: DOE/NSF Workshop on Correctness in Scientific Computing
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批准号:2319663
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项目类别:Standard Grant
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资助金额:$1.5万
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财政年份:2023
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负责人:Cindy Rubio Gonzalez
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依托单位:
Collaborative Research: PPoSS: LARGE: ScaleStuds: Foundations for Correctness Checkability and Performance Predictability of Systems at Scale
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批准号:2119348
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项目类别:Continuing Grant
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资助金额:$62.5万
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财政年份:2021
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负责人:Cindy Rubio Gonzalez
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依托单位:
CCRI: ENS: BugSwarm: Enhancing an Infrastructure and Dataset to Support the Software Engineering Research Community
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批准号:2016735
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项目类别:Standard Grant
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资助金额:$145.44万
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财政年份:2020
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负责人:Cindy Rubio Gonzalez
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依托单位:
CAREER: Understanding and Combating Numerical Bugs for Reliable and Efficient Software Systems
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批准号:1750983
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项目类别:Continuing Grant
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资助金额:$53.74万
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财政年份:2018
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负责人:Cindy Rubio Gonzalez
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依托单位:
CI-New: BugSwarm: A Large-Scale Repository of Replicable Defects, Tests, and Patches to Support the Software Engineering Research Community
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批准号:1629976
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项目类别:Standard Grant
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资助金额:$103.37万
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财政年份:2016
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负责人:Cindy Rubio Gonzalez
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依托单位:
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