CSR: Small: Automated Software Fault Localization via Static and Dynamic Analysis

CSR：小型：通过静态和动态分析自动软件故障定位

• 批准号: 1219080
• 负责人: Vikram Adve
• 金额: $ 49万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219080&HistoricalAwards=false
• 关键词: CSR; Small; Automated; Software; Fault

This project aims to improve tool support for software debugging, by providing automatic assistance in the identification of root causes of a software failure using static and dynamic program analysis techniques. A flexible diagnosis framework first identifies candidate root causes of a failure and then uses a broad, open-ended series of diagnosic "filters" to narrow down the most likely root causes. A key innovation is that the system automatically generates "passing" and "failing" inputs for diagnosis that are close to the original failing input, making it less likely that root causes are missed. The work develops two approaches for this difficult step, based on concolic testing and on string rewriting using the application's input grammar, and investigates various types of program properties ("likely invariants") for effective localization of various classes of bugs. Candidates are then winnowed using novel filters based on existing and new program analyses to pinpoint root causes with few false positives.This research will significantly increase programmer productivity by reducing manual debugging effort and improve reliability and security by reducing the time to fix critical software failures. A fully-automated diagnosis tool based on the widely-used LLVM compiler will be publicly distributed for developers to use. It will also be used for teaching debugging techniques and program analysis techniques in university classes.

该项目旨在通过使用静态和动态程序分析技术在识别软件故障的根本原因方面提供自动协助，来改进对软件调试的工具支持。灵活的诊断框架首先确定故障的候选根本原因，然后使用一系列广泛的、开放式的诊断“过滤器”来缩小最可能的根本原因。一个关键的创新是，该系统自动生成“通过”和“失败”的诊断输入，接近原始的失败输入，使其不太可能错过根本原因。 这项工作开发了两种方法，这个困难的一步，concolic测试和字符串重写使用应用程序的输入语法的基础上，并调查各种类型的程序属性（“可能的不变量”）的有效本地化的各类错误。候选人，然后使用基于现有的和新的程序分析，以查明根本原因与几个假阳性的新的过滤器筛选。这项研究将显着提高程序员的生产力，减少人工调试工作，并提高可靠性和安全性，减少修复关键软件故障的时间。基于广泛使用的LLVM编译器的全自动诊断工具将公开分发给开发人员使用。它还将用于大学课堂上的调试技术和程序分析技术的教学。