SHF: Small: Collaborative Research: Mera: Memoized Ranged Systematic Software Analyses

SHF：小型：协作研究：Mera：记忆范围系统软件分析

• 批准号: 1319688
• 负责人: Sarfraz Khurshid
• 金额: $ 35万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319688&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Mera; SHF; Small; Collaborative; Research; Mera

As software pervades our society and lives, failures due to softwarebugs become increasingly costly. Scalable approaches forsystematically checking software to find crucial bugs hold a key todelivering higher quality software at a lower cost. Mera is amethodology to scale model checking and symbolic execution which are twopowerful approaches for systematic software analysis and known to becomputationally expensive.The project builds on two novel concepts: memoization, which allowsre-using computations performed across different checks to amortizethe cost of software analysis; and ranging, which allows distributingthe analysis into sub-problems of lesser complexity, which can besolved separately and efficiently. Mera consists of three researchthrusts. First, the core memoization and ranging techniques for modelchecking and symbolic execution are developed. Second, thesetechniques are optimized in the context of different kinds of changes,like the program code, expected properties, or analysis search-depthparameters. Third, these techniques are adapted to effectivelyutilize available resources for parallel computation using static anddynamic strategies, such as work stealing. Mera will help improvesoftware quality and reliability thus holding the potential to providesubstantial economic benefits and to improve our quality of life.

随着软件遍布我们的社会和生活，由于软件次数造成的失败变得越来越成本。 可扩展的方法通过系统地检查软件以找到关键的错误，以较低的成本保存着关键的高质量软件。 梅拉（Mera）是范围模型检查和象征性执行的肢体方法学，这是对系统软件分析的二式方法，并且已知昂贵的方法。该项目以两个新颖的概念为基础：备忘录，该项目允许在不同的检查中执行的计算以延误软件分析成本；和范围，这允许将分析分配到较小复杂性的子问题中，这些问题可以分别有效地包围。 梅拉由三个研究性思想组成。 首先，开发了用于模型检查和符号执行的核心回忆和范围技术。 其次，在不同类型的更改的上下文中，将这些技术进行了优化，例如程序代码，预期属性或分析搜索搜索 - 访问范围。 第三，这些技术适应有效地利用静态和动态策略（例如窃取工作）的可行资源来并行计算。 Mera将有助于提高软件质量和可靠性，从而有可能提供实质性的经济利益并改善我们的生活质量。