Efficient Dynamic Analysis for Detecting and Tolerating Program Anomalies

用于检测和容忍程序异常的高效动态分析

• 批准号: 0811524
• 负责人: Kathryn McKinley
• 金额: $ 39.95万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811524&HistoricalAwards=false
• 关键词: Efficient; Dynamic; Analysis; Detecting; Tolerating

Businesses, transportation, economies, and science all increasingly depend on software. To manage these demands, programmers have turned to managed languages such as Java, Ruby, and C#, because they help produce higher quality software faster due to garbage collection and type safety. TOIBE fnds that most new software is using Java or other managed languages. Although these languages reduce errors, programs still contain memory and semantic errors. NIST estimates that software errors cost the US economy $59 billion a year. Because most prior tools for finding bugs slow programs down by factors and/or add substantial memory overheads, they are typically employed only during testing or after a crash, not during production.This research will explore new approaches for detecting, reporting, and sometimes tolerating errors that are efficient enough to use in deployed software. The techniques include new, low overhead compiler analyses, runtime instrumentation, and runtime algorithms. A key feature will be very low memory and modest time overheads that make them appealing for use in deployed software, especially in multicore where processes are competing for memory resources. A unique aspect will be that the garbage collector will summarize data structures in the heap to discover program patterns and anomalies. The proposed work will detect, report, and tolerate memory leaks that would otherwise crash the program. The research impact from this project will be higher quality software with fewer bugs that keeps executing correctly in spite of some errors, tools that help users by keeping software running, and tools that help developers find, diagnose, and fix errors. The investigator will make the tools publicly available, train graduate students, and mentor to increase minority participation in computer science.

商业、交通、经济和科学都越来越依赖于软件。 为了管理这些需求，程序员已经转向Java，Ruby和C#等托管语言，因为它们有助于更快地产生更高质量的软件，因为垃圾收集和类型安全。TOIBE发现大多数新软件都使用Java或其他托管语言。 虽然这些语言减少了错误，但程序仍然包含内存和语义错误。 NIST估计，软件错误每年给美国经济造成590亿美元的损失。 因为大多数以前的工具，发现错误慢下来的因素和/或增加大量的内存开销，他们通常只在测试期间或崩溃后，而不是在production.This研究将探索新的方法来检测，报告，有时容忍错误，是足够有效地使用在部署的软件。 这些技术包括新的、低开销的编译器分析、运行时插装和运行时算法。 一个关键的特性是非常低的内存和适度的时间开销，这使得它们在部署的软件中使用非常有吸引力，特别是在进程竞争内存资源的多核中。 一个独特的方面是垃圾收集器将总结堆中的数据结构，以发现程序模式和异常。 所提出的工作将检测，报告和容忍内存泄漏，否则将崩溃的程序。 该项目的研究影响将是更高质量的软件，具有更少的错误，尽管有一些错误，但仍能正确执行，帮助用户保持软件运行的工具，以及帮助开发人员发现，诊断和修复错误的工具。 研究人员将公开提供这些工具，培训研究生，并指导少数民族参与计算机科学。