Collaborative Research: Program Analysis Techniques to Support Dependable RTSJ Applications
协作研究:支持可靠 RTSJ 应用程序的程序分析技术
基本信息
- 批准号:0429149
- 负责人:
- 金额:$ 20.75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2004
- 资助国家:美国
- 起止时间:2004-09-15 至 2008-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
As part of the joint NSF/NASA High-Dependability Computing and CommunicationSystems Research effort, this project is developing, adapting and integrating a variety of program analysis techniques for the Real-timeSpecification for Java (RTSJ). RTSJ is an emerging language extensionfor Java that is designed for implementing concurrent time-criticalembedded software, for example, the control software on NASA'srobotic Mars rovers. This type of software is notoriously difficult totest and debug, and the model-checking, light-weight static and dynamicanalysis techniques being developed as part of this project promise toidentify bugs before software is deployed to the field and to producesystems that function more reliably.A cornerstone of this project is the use of controlled experimentation to judge the effectiveness of new analysis techniques. As part of this effort, RTSJ programs and experimental frameworks will be developed that supportrepeatable experimentation for determining analysis performance and the precision of analysis results. Experiment findings will drive the refinement ofanalysis techniques. Case studies using NASA testbed software will serveto evaluate the scalability and applicability to NASA-relevant softwarechallenges.If successful, this project promises to provide developers of next generation real-time embedded software, both at NASA and throughout the embeddedsystems domain, with a suite of powerful quality-assurance tools that will allow them to more cost-effectively produce higher-quality systems.Given the pace at which software is being embedded in our society'sinfra-structure, this could have broad impacts on the degree to which we can rely on such systems.
作为NSF/NASA高可靠性计算和通信系统研究项目的一部分,该项目正在为Java实时规范(RTSJ)开发、调整和集成各种程序分析技术。RTSJ是一种新兴的Java语言扩展,旨在实现并发的时间关键型嵌入式软件,例如,NASA机器人火星探测器上的控制软件。这类软件是出了名的难以测试和调试,而作为该项目一部分正在开发的模型检查、轻量级静态和动态分析技术有望在软件部署到现场之前识别出错误,并生产出功能更可靠的系统。该项目的基石是使用受控实验来判断新分析技术的有效性。作为这项工作的一部分,RTSJ程序和实验框架将开发,重复实验,以确定分析性能和分析结果的精度。实验结果将推动分析技术的完善。使用NASA测试平台软件的案例研究将评估NASA相关软件挑战的可扩展性和适用性。如果成功,该项目将为NASA和整个嵌入式系统领域的下一代实时嵌入式软件开发人员提供支持。一套强大的质量保证工具,使他们能够更经济有效地生产更高,考虑到软件嵌入我们社会基础设施的速度,这可能会对我们依赖这些系统的程度产生广泛的影响。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Matthew Dwyer其他文献
Design guide for small-scale local facilities
小型当地设施设计指南
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
S. Oostermeijer;Matthew Dwyer - 通讯作者:
Matthew Dwyer
Wireless <em>in vivo</em> recording of cortical activity by an ion-sensitive field effect transistor
- DOI:
10.1016/j.snb.2023.133549 - 发表时间:
2023-05-01 - 期刊:
- 影响因子:
- 作者:
Suyash Bhatt;Emily Masterson;Tianxiang Zhu;Jenna Eizadi;Judy George;Nesya Graupe;Adam Vareberg;Jack Phillips;Ilhan Bok;Matthew Dwyer;Alireza Ashtiani;Aviad Hai - 通讯作者:
Aviad Hai
Wireless emin vivo/em recording of cortical activity by an ion-sensitive field effect transistor
基于离子敏感场效应晶体管的皮质活动在体内的无线记录
- DOI:
10.1016/j.snb.2023.133549 - 发表时间:
2023-05-01 - 期刊:
- 影响因子:7.700
- 作者:
Suyash Bhatt;Emily Masterson;Tianxiang Zhu;Jenna Eizadi;Judy George;Nesya Graupe;Adam Vareberg;Jack Phillips;Ilhan Bok;Matthew Dwyer;Alireza Ashtiani;Aviad Hai - 通讯作者:
Aviad Hai
Matthew Dwyer的其他文献
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{{ truncateString('Matthew Dwyer', 18)}}的其他基金
SHF: Small: Distribution-aware Testing for Neural Networks
SHF:小型:神经网络的分布感知测试
- 批准号:
2129824 - 财政年份:2021
- 资助金额:
$ 20.75万 - 项目类别:
Standard Grant
FMitF: Track I: Focusing Incremental Abstraction-based Verification on Neural Networks Input Distributions
FMITF:第一轨:专注于神经网络输入分布的增量抽象验证
- 批准号:
2019239 - 财政年份:2020
- 资助金额:
$ 20.75万 - 项目类别:
Standard Grant
SHF: Medium: Rearchitecting Neural Networks for Verification
SHF:中:重新架构神经网络进行验证
- 批准号:
1900676 - 财政年份:2019
- 资助金额:
$ 20.75万 - 项目类别:
Continuing Grant
SHF: Small: Measurable Program Analysis
SHF:小型:可衡量的计划分析
- 批准号:
1901769 - 财政年份:2018
- 资助金额:
$ 20.75万 - 项目类别:
Standard Grant
SHF: Small: Measurable Program Analysis
SHF:小型:可衡量的计划分析
- 批准号:
1617916 - 财政年份:2016
- 资助金额:
$ 20.75万 - 项目类别:
Standard Grant
SHF: EAGER: Collaborative Research: Mapping Software Analysis Problems to Efficient and Accurate Constraints
SHF:EAGER:协作研究:将软件分析问题映射到高效、准确的约束
- 批准号:
1449626 - 财政年份:2014
- 资助金额:
$ 20.75万 - 项目类别:
Standard Grant
CSR-EHS Predictable Adaptive Residual Monitoring for Real-time Embedded Systems
适用于实时嵌入式系统的 CSR-EHS 可预测自适应残留监测
- 批准号:
0720654 - 财政年份:2007
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$ 20.75万 - 项目类别:
Continuing Grant
Collaborative Research: Finite-State Verification for High-Performance Computing
协作研究:高性能计算的有限状态验证
- 批准号:
0541263 - 财政年份:2006
- 资助金额:
$ 20.75万 - 项目类别:
Continuing Grant
BOGOR : A Model Checking Framework for Dynamic Software
BOGOR:动态软件的模型检查框架
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0444167 - 财政年份:2004
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$ 20.75万 - 项目类别:
Standard Grant
BOGOR : A Model Checking Framework for Dynamic Software
BOGOR:动态软件的模型检查框架
- 批准号:
0306607 - 财政年份:2003
- 资助金额:
$ 20.75万 - 项目类别:
Standard Grant
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