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Collaborative Research: SHF: Small: A General Framework for Responsive Static Analysis

Collaborative Research: SHF: Small: A General Framework for Responsive Static Analysis
合作研究:SHF:小型:响应式静态分析的通用框架
批准号:
2223825
负责人:
Bor-Yuh Evan Chang
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-10-01 至 2025-09-30

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中文摘要
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英文摘要
Society increasingly relies on the reliability and security of software. Abstract interpretation is a well-established methodology for proving that software is free of certain classes of bugs. However, for industrial-scale software, standard abstract interpretation techniques may take hours to complete, making them difficult to integrate into modern software development practices. This project develops a framework for responsive static analysis, which retains the power of abstract interpretation while running much more quickly for common use cases. The project's novelties are new algorithms for running abstract interpretation responsively, corresponding mathematical proofs that these algorithms produce the desired, correct results, and working implementations of the algorithms. The project's impacts are greater performance and applicability of powerful abstract interpretation techniques for verifying software correctness, which in turn will yield more reliable and secure software.The project builds on a recently-developed framework for demanded abstract interpretation, a demand-driven and incremental analysis approach based on reifying analysis computations and dependencies in a graph structure. Via generalizations of this approach, this project will extend the framework to handle compositional analysis, essential for efficient analysis of procedure calls, and refinement-based analysis, to enable combining analyses with varying levels of precision and scalability. This generalized framework will facilitate provable guarantees of from-scratch consistency, a crucial property for responsive analysis. The project will also implement the generalized framework and instantiate it with challenging analysis problems, addressing research challenges in making the framework practical.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.
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SHF: Small: Programming with Semantic Revision Requests
  • 批准号:
    2008369
  • 项目类别:
    Standard Grant
  • 资助金额:
    $49.92万
  • 财政年份:
    2020
  • 负责人:
    Bor-Yuh Evan Chang
  • 依托单位:
IUCRC Planning University of Colorado Boulder: Center for Pervasive Personalized Intelligence (PPI)
  • 批准号:
    1822135
  • 项目类别:
    Standard Grant
  • 资助金额:
    $1.5万
  • 财政年份:
    2018
  • 负责人:
    Bor-Yuh Evan Chang
  • 依托单位:
SHF: Small: Collaborative Research: Online Verification-Validation
  • 批准号:
    1619282
  • 项目类别:
    Standard Grant
  • 资助金额:
    $31.0万
  • 财政年份:
    2016
  • 负责人:
    Bor-Yuh Evan Chang
  • 依托单位:
SHF: Small: Modular Reflection
  • 批准号:
    1218208
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.0万
  • 财政年份:
    2012
  • 负责人:
    Bor-Yuh Evan Chang
  • 依托单位:
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
Cell Research
Cell Research (细胞研究)