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
• 负责人: Matthew Dwyer
• 金额: $ 7.5万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1449626&HistoricalAwards=false
• 关键词: SHF; EAGER; Collaborative; Research; Mapping

Techniques for finding faults in software systems, such as crashes, security vulnerabilities, and deadlocks, have become increasingly powerful over the past two decades. This is due in no small part to the development of efficient automated satisfiability solvers. The interest in applying these solvers to an ever wider class of software analysis applications has pushed solvers to their limits. As a result, analysis developers are currently forced to approximate analysis?s queries to make use of existing solvers. Because of this software analyses can mistakenly diagnose an error, miss reporting a true error, and suffer unnecessarily poor performance. This research seeks to establish accuracy as an important missing dimension of solver support and its success will lead to broader and more cost-effective use of solvers to produce high-quality software.This project is the first to systematically explore and link the accuracy requirements of a software analysis to the accuracy provided by a solver. This project does this by exploring approaches to specify the accuracy requirements of solver clients and detect, recover and report solution accuracy for integer and string constraints. These capabilities are being implemented in an existing solver interface framework, called Green, which is applied to perform symbolic execution of Java programs, using Symbolic Pathfinder. The project will evaluate the extent to this approach simplifies client analysis development, enables clients to use a variety of solvers - even those that do not perfectly match accuracy requirements, and improves analysis performance.

在过去的二十年中，查找软件系统中的故障（如崩溃、安全漏洞和死锁）的技术变得越来越强大。这在很大程度上要归功于高效的自动化满意度求解器的发展。将这些求解器应用于更广泛的软件分析应用程序的兴趣已经将求解器推向了它们的极限。因此，分析开发人员目前被迫近似分析？S查询来利用现有的求解器。因此，软件分析可能会错误地诊断错误，错过报告真正的错误，并遭受不必要的性能低下。这项研究试图建立准确性作为求解器支持的一个重要缺失维度，它的成功将导致求解器更广泛和更经济地使用，以生产高质量的软件。该项目是第一个系统地探索并将软件分析的精度要求与求解器提供的精度联系起来的项目。该项目通过探索方法来指定求解器客户端的精度要求，并检测、恢复和报告整数和字符串约束的解决方案精度。这些功能已经在一个名为Green的现有求解器接口框架中实现，该框架使用symbolic Pathfinder来执行Java程序的符号执行。该项目将评估该方法简化客户分析开发的程度，使客户能够使用各种求解器——即使是那些不能完全满足精度要求的求解器，并提高分析性能。