Component Data-Flow Analysis for Large-Software Systems

大型软件系统的组件数据流分析

• 批准号: 9900988
• 负责人: Barbara Ryder
• 金额: $ 30.1万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至 2003-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9900988&HistoricalAwards=false
• 关键词: Component; Data; Flow; Analysis; Large

9900988 Barbara RyderLarge industrial-sized software systems (of 1 million lines of code) suffer from a lack of adequate testing and program understanding tools, especially when such systems contain heterogeneous components and/or legacy codes. Scalable compile-time analysis techniques are needed to serve as a basis for appropriate software tools which can provide adjustable levels of precision for predictable cost. Analysis results should be obtainable for an arbitrary component (e.g., module) of a system, even if the code for the entire system is not available for inspection. The goal of this research is to explore new analyses applicable to these large industrial systems, emphasizing the issues of scalability and unavailability of full system source code. This will be accomplished by design of a schema for "component data-flow analysis", a scalable data-flow technique which, given some coarse-grained data-flow information about unavailable program components, is able to apply an acceptably precise data-flow analysis on a particular component whose source code is available. The utility of component analysis will be demonstrated by its application to several data-flow problems useful for testing and program understanding, using a prototype implementation built with the PROLANGS Analysis Framework (PAF), a publically available software platform for compile-time program analysis

大型工业规模的软件系统（100万行代码）缺乏足够的测试和程序理解工具，特别是当这些系统包含异构组件和/或遗留代码时。需要可伸缩的编译时分析技术作为适当的软件工具的基础，这些软件工具可以提供可调整的精度水平以实现可预测的成本。 分析结果应该是可获得的任意组件（例如，模块），即使整个系统的代码无法用于检查。 本研究的目标是探索适用于这些大型工业系统的新分析，强调完整系统源代码的可扩展性和不可用性问题。 这将是通过设计一个“组件数据流分析”，一个可扩展的数据流技术，给定一些粗粒度的数据流信息不可用的程序组件，是能够应用一个可接受的精确的数据流分析的模式，其源代码是可用的特定组件。 组件分析的实用性将通过其应用于几个对测试和程序理解有用的数据流问题来证明，使用PROLANGS分析框架（PAF）构建的原型实现，PAF是一个可用于编译时程序分析的软件平台