ExPert: dynamic analysis based fault location via Execution Perturbations

ExPert：通过执行扰动进行基于动态分析的故障定位

• 批准号: 0614707
• 负责人: Rajiv Gupta
• 金额: --
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0614707&HistoricalAwards=false
• 关键词: ExPert; dynamic; analysis; based; fault

ExPert: dynamic analysis based fault location via Execution PerturbationsAbstractThis project is studying dynamic characteristics of runs of faulty programs anddesigning promising dynamic analysis techniques to identify executed programstatements where a fault lies, i.e. the fault candidate set. To narrow the set ofstatements in the fault candidate set, an array of automated dynamic analyses arebeing used in conjunction with each other. These analyses consider not only thefailed run but also execution perturbations of this faulty run. The goal of thiswork is to focus the users attention on a small subset of statements in thefault candidate state. Identification of fault candidate set is based uponuncovering of two types of evidence: negative evidence and positive evidence. A negative evidence is considered as an indicator that leads to the conclusion that a value encountered during a failed program run is related to the program's failure. Positive evidence on the other hand indicates that some of the values computed during a failed run are in fact correct. Through a combination of negativeand positive evidence we aim to produce a small fault candidate set. As part of this work we are identifying new sources of evidence. We are also carrying out a systematic integration of above dynamic analyses technology into a practical debugging environment.

实验：基于执行扰动的动态分析故障定位本项目研究故障程序运行的动态特性，设计有前途的动态分析技术来识别故障所在的执行程序语句，即故障候选集。为了缩小故障候选集中的语句集，一系列自动化动态分析正在相互结合使用。这些分析不仅考虑了失败的运行，而且还考虑了这个错误运行的执行扰动。这项工作的目标是把用户的注意力集中在一个小的子集的陈述在thefault候选状态。故障候选集的识别基于两类证据的发现：否定证据和肯定证据。 负面证据被认为是一个指标，导致在失败的程序运行期间遇到的值与程序的失败有关的结论。另一方面，积极的证据表明，在失败的运行期间计算的一些值实际上是正确的。通过结合否定和肯定的证据，我们的目标是产生一个小的故障候选集.作为这项工作的一部分，我们正在确定新的证据来源。我们还将上述动态分析技术系统地集成到实际的调试环境中。