CAREER: Advancing Neural Testing and Debugging of Software

职业:推进软件的神经测试和调试

基本信息

项目摘要

Software is an integral part of every life, from cell phones in everyone's pocket to autonomous cars that have already driven millions of miles to embedded software enabling smart home appliances. Developers are prone to making mistakes and introducing bugs to the software, making automated software validation techniques essential to ensure delivering reliable software. Software testing is the activity of finding and fixing software bugs is an important activity for software developers. With the advent of Artificial Intelligence (AI) and the potential power of Machine Learning (ML) in understanding and predicting bug patterns in code, software testing and debugging are gradually moving towards learning-based techniques, i.e., neural testing and debugging of software. This research project will address fundamental challenges in automated software testing and debugging by leveraging AI, and will develop new insights for semantically robust and interpretable neural testing and debugging.Combining theory building, empirical data-driven research, and tool building, this research aims to (1) design semantically robust neural models of code and develop systematic approaches for high-quality dataset generation, (2) develop several techniques to construct deep test oracles for functional and non-functional testing, and (3) design interpretation techniques for extracting and reusing the knowledge of neural models to unify testing and debugging. These overarching ideas can make software testing and debugging smarter and faster, significantly impacting how researchers and practitioners improve software quality.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.
软件是每个人生活中不可或缺的一部分,从每个人口袋里的手机到已经行驶了数百万英里的自动驾驶汽车,再到支持智能家电的嵌入式软件。开发人员很容易犯错误并将bug引入软件,因此自动化软件验证技术对于确保交付可靠的软件至关重要。软件测试是发现和修复软件缺陷的活动,是软件开发人员的一项重要活动。随着人工智能(AI)的出现和机器学习(ML)在理解和预测代码中错误模式方面的潜在能力,软件测试和调试正逐渐转向基于学习的技术,即,软件的神经测试和调试。该研究项目将利用人工智能解决自动化软件测试和调试的基本挑战,并将开发语义健壮和可解释的神经测试和调试的新见解。该研究结合理论构建,经验数据驱动研究和工具构建,旨在(1)设计语义健壮的代码神经模型并开发高质量数据集生成的系统方法,(2)开发几种技术来构建用于功能和非功能测试的深度测试预言机,(3)设计用于提取和重用神经模型知识的解释技术,以统一测试和调试。这些总体思想可以使软件测试和调试更智能、更快速,显著影响研究人员和从业人员如何提高软件质量。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响评审标准进行评估来支持。

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)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Reyhaneh Jabbarvand其他文献

Nemo: Multi-criteria Test-Suite Minimization with Integer Nonlinear Programming
Nemo:使用整数非线性规划的多标准测试套件最小化
CodeMind: A Framework to Challenge Large Language Models for Code Reasoning
CodeMind:挑战代码推理大型语言模型的框架
  • DOI:
    10.48550/arxiv.2402.09664
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Changshu Liu;Shizhuo Dylan Zhang;Reyhaneh Jabbarvand
  • 通讯作者:
    Reyhaneh Jabbarvand
A Generic Approach to Fix Test Flakiness in Real-World Projects
修复实际项目中测试不稳定的通用方法
  • DOI:
    10.48550/arxiv.2404.09398
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yang Chen;Reyhaneh Jabbarvand
  • 通讯作者:
    Reyhaneh Jabbarvand

Reyhaneh Jabbarvand的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

相似海外基金

Advancing human neural progenitor cells (hNPCs) to FDA IND approval
推动人类神经祖细胞 (hNPC) 获得 FDA IND 批准
  • 批准号:
    10642228
  • 财政年份:
    2023
  • 资助金额:
    $ 58.29万
  • 项目类别:
Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications
合作研究:III:媒介:异质数据的图神经网络:推进理论、模型和应用
  • 批准号:
    2406648
  • 财政年份:
    2023
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Standard Grant
Advancing Neural Network Models of Language Processing
推进语言处理的神经网络模型
  • 批准号:
    RGPIN-2017-06310
  • 财政年份:
    2022
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Discovery Grants Program - Individual
Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications
合作研究:III:媒介:异质数据的图神经网络:推进理论、模型和应用
  • 批准号:
    2212143
  • 财政年份:
    2022
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Standard Grant
Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications
合作研究:III:媒介:异质数据的图神经网络:推进理论、模型和应用
  • 批准号:
    2212144
  • 财政年份:
    2022
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Standard Grant
Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications
合作研究:III:媒介:异质数据的图神经网络:推进理论、模型和应用
  • 批准号:
    2212145
  • 财政年份:
    2022
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Standard Grant
Advancing Federated Learning of Neural Networks for Medical Imaging
推进医学成像神经网络的联合学习
  • 批准号:
    2594573
  • 财政年份:
    2021
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Studentship
Advancing Neural Network Models of Language Processing
推进语言处理的神经网络模型
  • 批准号:
    RGPIN-2017-06310
  • 财政年份:
    2021
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Discovery Grants Program - Individual
Advancing understanding of neural representations of threat perception through a novel predictive coding framework
通过新颖的预测编码框架增进对威胁感知的神经表征的理解
  • 批准号:
    10418765
  • 财政年份:
    2020
  • 资助金额:
    $ 58.29万
  • 项目类别:
Advancing Neural Network Models of Language Processing
推进语言处理的神经网络模型
  • 批准号:
    RGPIN-2017-06310
  • 财政年份:
    2020
  • 资助金额:
    $ 58.29万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了