SHF: Small: Automatically Localizing Functional Faults In Deployed Software Applications
SHF:小型:自动定位已部署软件应用程序中的功能故障
基本信息
- 批准号:1615563
- 负责人:
- 金额:$ 35.09万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-07-15 至 2021-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Even though most software applications are tested before they are released to customers, these applications still contain production (or field) functional faults that result in field failures, which have costly consequences and are expensive to fix. Due to their limitations, existing automatic debugging approaches do not adequately isolate and identify production faults for field failures. Prior interviews of test managers and studies of bug repositories revealed that programmers spent close to 50% of their time on average to localize production faults, which is a major factor in software system and software project failures. The educational innovation of this project is in developing an integrated approach to teaching by applying probabilistic graphical models to software engineering problems. The goal of this proposal is to create a novel theoretical foundation that allows stakeholders to predict and localize functional faults for field failures automatically with a high degree of precision using symptoms only (e.g., the sign of the output value is incorrect) and without instrumenting deployed applications to collect runtime data, thus avoiding the deployment runtime overhead, and without having any tests with oracles to uncover the fault, without performing contrasting successful and failed runs, and without collecting runtime data from field failures. With this theoretical foundation, researchers can collaborate more closely in planning the future of fault localization by expanding each other's results based on probabilistic graphical models as common abstractions. Based only on failure symptoms occurring during deployment of a given application, the location of faults in the source code will be determined, as well as navigation paths from likely faults to the code that can fix these faults. The project will create, evaluate and deploy: (1) new theories, algorithms and techniques for automatically obtaining probabilistic graphical models that approximate specific fault models for software applications; (2) a novel way in which model-based differential diagnoses are used to perform abductive reasoning to localize production faults given symptoms for field failures, and (3) a comprehensive experimentation framework for evaluating the effectiveness of the algorithms for localizing production faults. In addition to localizing production functional faults, the implementation can be used as a broad experimental platform for creating and testing hypotheses for various software debugging and testing ideas, e.g., for guiding test selection and prioritization.
尽管大多数软件应用程序在发布给客户之前都经过了测试,但这些应用程序仍然包含导致现场故障的生产(或现场)功能故障,这会造成昂贵的后果并且修复起来也很昂贵。由于其局限性,现有的自动调试方法不能充分隔离和识别现场故障的生产故障。 之前对测试经理的采访和对 bug 存储库的研究表明,程序员平均花费近 50% 的时间来定位生产故障,这是软件系统和软件项目失败的一个主要因素。该项目的教育创新在于通过将概率图形模型应用于软件工程问题来开发一种集成的教学方法。该提案的目标是创建一个新颖的理论基础,使利益相关者能够仅使用症状(例如,输出值的符号不正确)而高精度地自动预测和定位现场故障的功能故障,而无需部署仪器应用程序来收集运行时数据,从而避免了部署运行时开销,并且无需使用预言机进行任何测试来发现故障,无需执行对比成功和失败的运行,也无需从现场故障中收集运行时数据。有了这个理论基础,研究人员可以通过基于概率图模型作为通用抽象来扩展彼此的结果,从而在规划故障定位的未来时更密切地合作。 仅根据给定应用程序部署期间发生的故障症状,即可确定源代码中的故障位置,以及从可能的故障到可以修复这些故障的代码的导航路径。该项目将创建、评估和部署:(1)新的理论、算法和技术,用于自动获取近似软件应用程序特定故障模型的概率图形模型; (2)一种新颖的方法,其中基于模型的鉴别诊断用于执行溯因推理,以在给定现场故障症状的情况下定位生产故障,以及(3)用于评估定位生产故障的算法有效性的综合实验框架。除了定位生产功能故障之外,该实现还可以用作广泛的实验平台,用于创建和测试各种软件调试和测试想法的假设,例如,用于指导测试选择和优先级排序。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
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Mark Grechanik其他文献
Testing software in age of data privacy: a balancing act
数据隐私时代的软件测试:平衡之举
- DOI:
10.1145/2025113.2025143 - 发表时间:
2011-09-05 - 期刊:
- 影响因子:0
- 作者:
Kunal Taneja;Mark Grechanik;Rayid Ghani;Tao Xie - 通讯作者:
Tao Xie
Mark Grechanik的其他文献
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{{ truncateString('Mark Grechanik', 18)}}的其他基金
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- 批准号:
2129739 - 财政年份:2022
- 资助金额:
$ 35.09万 - 项目类别:
Standard Grant
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2129739 - 财政年份:2022
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$ 35.09万 - 项目类别:
Standard Grant
SHF:Small:Proving User Interface Testing Programs Correct
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2120142 - 财政年份:2021
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$ 35.09万 - 项目类别:
Standard Grant
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- 批准号:
1908094 - 财政年份:2019
- 资助金额:
$ 35.09万 - 项目类别:
Standard Grant
EAGER: Securing Smartphone Applications Against Rapidly Expanding Accessibility-Based Attacks
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- 批准号:
1650000 - 财政年份:2016
- 资助金额:
$ 35.09万 - 项目类别:
Standard Grant
I-Corps: Automatically Localizing Functional Faults In Deployed Software Applications
I-Corps:自动定位已部署软件应用程序中的功能故障
- 批准号:
1547597 - 财政年份:2015
- 资助金额:
$ 35.09万 - 项目类别:
Standard Grant
Travel Support For ACM/IEEE International Conference on Software Engineering (ICSE 2014)
ACM/IEEE 软件工程国际会议 (ICSE 2014) 差旅支持
- 批准号:
1360923 - 财政年份:2014
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$ 35.09万 - 项目类别:
Standard Grant
III: Small: Collaborative Research: Linking Evolving Software Requirements and Acceptance Tests
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1217928 - 财政年份:2012
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$ 35.09万 - 项目类别:
Standard Grant
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- 批准号:
1017633 - 财政年份:2010
- 资助金额:
$ 35.09万 - 项目类别:
Continuing Grant
III: Small: Collaborative Research: Creating and Evolving Software via Searching, Selecting and Synthesizing Relevant Source Code
III:小:协作研究:通过搜索、选择和综合相关源代码来创建和发展软件
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0916139 - 财政年份:2009
- 资助金额:
$ 35.09万 - 项目类别:
Standard Grant
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