结合用例约简与联合依赖概率建模的错误定位

现有的测试用例约简方法不能有效提高错误定位精度,现有的软件错误定位方法不能充分分析元素间的依赖关系.针对以上问题,提出结合测试用例约简和联合依赖概率建模的软件错误自动定位方法,将测试用例约简与软件错误定位统一为一个整体.不同于一般的测试用例约简方法,所提出的测试用例约简方法在程序执行路径的基础上充分考虑了错误测试用例对错误定位的影响,能够为错误定位提供有效的测试用例,为快速、准确地定位软件错误奠定基础.定义了一种新的统计模型——联合依赖概率模型,充分分析了程序元素间的控制依赖、数据依赖以及语句执行状态,并提出基于联合依赖概率模型的错误自动定位方法.通过计算联合依赖关系的可疑度,对可疑节点进行排序,准确定位错误语句.实验结果表明：与SBI,SOBER,Tarantula,SF和RankCP方法相比,该算法可以更加有效地定位软件错误.     

自修复数据库选择性恢复机制

选择性恢复使得自修复数据库在发生用户错误操作或入侵事务导致的故障之后,只撤销错误操作或入侵事务以及受感染的可疑事务,而保留未受感染的合法事务.提出一种新的选择性恢复机制,使用事务依赖日志、写操作日志和事务语句日志,分别记录事务间的依赖关系、事务写操作的前像数据和事务执行语句,在故障发生后,可以使数据库恢复到故障发生前一...     

云资源池探针的故障检测方法研究

随着大数据时代的发展,人们越来越重视保存历史数据,通过海量的历史数据挖掘数据潜藏的价值。在云计算与大数据发展的背景下,虚拟化资源产生的大量状态文件、日志信息将为云平台的故障诊断与预测提供了有力地预测与快速定位依据。通过建立探针机制的挖掘虚拟化资源池日志等信息,找出其中潜在的关联信息。本文构建云计算资源池的大数据分析模型,通过利用大数据技术对资源池各管理层日志进行关联性分析,快速对故障和性能瓶颈进行探查,准确给出探查出的每层资源故障路径或性能瓶颈轨迹,以智能化的方式替换随机性和经验性的人工模式。通过注入云计算操作系统的控制台接口和日志、利用SDN技术对网络流量进行采集、通过存储虚拟化控制工具采集存储性能和日志,再结合现有应用性能分析系统API接口开发,研发基于探针技术的资源池故障及性能分析探针系统。通过该模型系统能对不同资源类型的数据和日志进行综合分析,从这些数据中捕获隐患信息和故障发生点的位置信息并予以可视化表示。     

业务流程隐变迁的拟间接依赖挖掘方法

隐变迁存在于业务流程中,但在日志中未被记录,挖掘隐变迁能够还原模型并提高流程的运行效率。已有方法都是基于日志间直接依赖关系挖掘隐变迁,未考虑其间接依赖关系,具有一定的局限性。提出基于拟间接依赖关系挖掘隐变迁的方法,根据事件日志中活动间的轮廓关系构建初始模型,通过拟间接依赖关系表找出日志序列之间的约束体。利用整数线性规划方法,查找符合要求的拟间接关系变迁对,从而挖掘出拟间接关系变迁对中存在的隐变迁。将隐变迁融合到初始模型中,得到含有隐变迁的目标模型。通过具体的实例分析验证了该方法的有效性。     

基于故障定位集的无线传感器网络故障检测

由于无线传感器网络(WSN)节点的位置分布广,网络出现故障后如何进行故障检测以实现功能的恢复显得尤其困难,因此,探索快速有效的网络故障检测方法对于大规模WSN的可靠应用很有必要;基于最小故障定位集,提出了一种新的故障定位和故障检测方法;该方法在WSN分布式结构模型基础上,将网络节点与节点间的连接关系以图的方式进行描述,利用最短路径分析方法,来对WSN中出现的故障进行定位和检测;应用分析表明,该算法可以快速有效实现WSN故障定位与检测.     

一种基于准同步检查点的虚拟机卷回恢复算法

针对典型的云平台下虚拟化系统的特点,提出了一种结合选择性日志的准同步检查点算法VM_QSC:保持不同虚拟机节点固有的优化检查点周期,通过物理节点Hypervisor选择性地进行虚拟机的消息日志的稳定存储,在全局监控节点维护虚拟机一致线信息,保持全局的一致性。与传统的准同步检查点和同步检查点相比,该算法维持了虚拟机检查点设置的自主性,并显著降低了虚拟化系统的容错开销,可以有效应用于云计算环境下的虚拟资源管理和动态迁移。     

CNGI-MSN中存储子节点管理系统的设计与实现

CNGI-MSN（CNGI Mass Storage Network）是国家CNGI项目支持的面向下一代互联网的PB级智能网络存储系统.本文介绍了CNGI-MSN中清华存储子节点管理系统的设计思想和实现细节.该系统实现存储资源虚拟化、性能监测、系统信息的结构化存储、故障报告、日志管理、用户访问管理和节点自动发现七大功能,能够做到可靠、高效和安全的管理存储资源.作为CNGI-MSN的关键组成部分,该系统已经成功地应用于清华存储子节点的管理,并对远程的CNGI-MSN全域管理系统提供可靠、高效的支持.     

协作式无线传感器网络节点定位算法的研究

提出了一种在三个锚节点的条件下基于到达角的测距方法,利用节点之间的几何关系实现传感器网络中未知节点的定位。通过仿真验证该方法能够对未知节点进行有效的定位;同时,该方法仅依赖到达角信息来确定节点的位置,从而降低了处理的复杂度、节省了节点的能量、提高了节点的寿命。     

基于贝叶斯网络的分层网络故障诊断

本文以SNMP网络管理模型的管理信息库（MIB）为基础,在不同层次上构建了用于故障判别与定位的贝叶斯网络。对MIB变量采用自适应自回归（AAR）模型建模分析,构建与其相关协议之间的贝叶斯网络,推断协议功能是否发生异常。分析各个协议之间的功能依赖关系,构建协议间的贝叶斯网络,定位协议间的故障根源。考虑网络中故障传播构建了基于网络拓扑的贝叶斯网,定位故障根源节点。最后,对构建的模型进行了实验仿真,并分析了模型的优点和缺点。     

基于Hadoop的Web日志挖掘

基于单一节点的数据挖掘系统在挖掘Web海量数据源时存在计算瓶颈,针对该问题,利用云计算的分布式处理和虚拟化技术的优势,设计一种基于云计算的Hadoop集群框架的Web日志分析平台,提出一种能够在云计算环境中进行分布式处理的混合算法。为进一步验证该平台的高效性,在该平台上利用改进后的算法挖掘Web日志中用户的偏爱访问路径。实验结果表明,在集群中运用分布式算法处理大量的Web日志文件,可以明显提高Web数据挖掘的效率。     

IPSETFUL: an iterative process of selecting test cases for effective fault localization by exploring concept lattice of program spectra

Fault localization is an important and challenging task during software testing. Among techniques studied in this field, program spectrum based fault localization is a promising approach. To perform spectrum based fault localization, a set of test oracles should be provided, and the effectiveness of fault localization depends highly on the quality of test oracles. Moreover, their effectiveness is usually affected when multiple simultaneous faults are present. Faced with multiple faults it is difficult for developers to determine when to stop the fault localization process. To address these issues, we propose an iterative fault localization process, i.e., an iterative process of selecting test cases for effective fault localization (IPSETFUL), to identify as many faults as possible in the program until the stopping criterion is satisfied. It is performed based on a concept lattice of program spectrum (CLPS) proposed in our previous work. Based on the labeling approach of CLPS, program statements are categorized as dangerous statements, safe statements, and sensitive statements. To identify the faults, developers need to check the dangerous statements. Meantime, developers need to select a set of test cases covering the dangerous or sensitive statements from the original test suite, and a new CLPS is generated for the next iteration. The same process is proceeded in the same way. This iterative process ends until there are no failing tests in the test suite and all statements on the CLPS become safe statements. We conduct an empirical study on several subject programs, and the results show that IPSETFUL can help identifymost of the faults in the program with the given test suite. Moreover, it can save much effort in inspecting unfaulty program statements compared with the existing spectrum based fault localization techniques and the relevant state of the art technique.     

基于内存更新记录的漏洞攻击错误定位方法

软件漏洞攻击威胁日益严重.其中基于内存腐败漏洞的攻击最为普遍,如缓冲区溢出和格式化串漏洞.提出一种针对内存腐败漏洞攻击的自动错误定位方法.基于内存更新操作记录,可以回溯找到程序源代码中腐败关键数据的语句,从而提供有益的信息修复漏洞并生成最终补丁.     

基于路径分析和信息熵的错误定位方法

软件错误定位是一项耗时又费力的工作,因此如何提高软件错误定位的自动化程度一直以来都是软件工程领域研究的热点.现有的基于频谱的错误定位方法很少利用程序的上下文信息,而程序的上下文信息对错误定位至关重要.针对此问题,本文提出了一种基于路径分析和信息熵的错误定位方法FLPI.该方法在基于频谱信息技术的基础上,通过对所有执行路径中的数据依赖关系进行分析来引入执行上下文信息,同时利用信息熵理论将测试事件信息引入到可疑语句的怀疑度计算公式中,以提高错误定位的精度和效率.为了评价该方法的有效性,基于一组基准程序和开源程序进行实验验证.实验结果表明,本文所提方法FLPI能够有效地提高错误定位的精度和效率.     

一种新型的故障检测定位方法研究

针对大型复杂系统故障定位耗时长等问题,在分析故障树特征的基础上,改进了遗传算法,设计了基于故障树底事件的故障检测定位方法。该方法将顺序检测融入到改进的遗传算法检测当中,在工程应用领域体现了一定的优越性。仿真实验结果表明,该方法能够准确定位故障,有助于提高故障检测定位的效率。     

The concept of residuals for fault localization in discrete event systems

In this paper an approach for fault localization in closed-loop Discrete Event Systems is proposed. The presented diagnosis method allows fault localization using a fault-free system model to describe the expected system behavior. Via a systematic comparison of the observed and the expected behavior, a fault can be detected and a set of fault candidates is determined. Inspired by residuals known from diagnosis in continuous systems, different set operations are introduced to generate the fault candidate set. After fault detection and a first fault localization, a procedure is given to render the fault localization more precisely by an analysis of the further observed system behavior. Special emphasis is given to the use of identified models for the fault-free system behavior. The approach is explained using a laboratory manufacturing facility.     

Model-based Fault Localization: Finding Behavioral Outliers in Large-scale Computing Systems

We present a model-based approach to fault localization that aims to help the human analyst narrow down the manual localization into a small fraction of the overall system. Our method consists of two parts: pre-failure model derivation and post-failure model-based anomaly detection. The first part collects function-call traces from all processes and derives an execution model that reflects the function-calling behaviors of the target system. When a failure occurs, we identify the most deviant behaviors in the failed run by comparing the failure traces with the derived model. We claim that the analyst can substantially reduce the burden of fault localization by prioritizing such behaviors. Our preliminary experiment with a distributed job manager supports this claim: Our method narrows down localization of a 70-second faulty run on a 78-node distributed platform into just sub-second behaviors involving only two nodes.     

自适应推导下的统一化调试加速技术

在传统调试过程中,缺陷定位通常作为程序修复的前置步骤.最近,一种新型调试框架(统一化调试)被提出.不同于传统调试中缺陷定位和程序修复的单向连接方式,统一化调试首次建立了定位与修复之间的双向连接机制,从而达到同时提升两个领域的效果.作为首个统一化调试技术, ProFL利用程序修复过程中伴随产生的大量补丁执行信息逆向地提升已有缺陷定位技术的效果.统一化调试技术不仅修复了可被修复的缺陷,而且也为不能被自动修复技术修复的缺陷提供了有效的调试线索.虽然统一化调试是一个很有前景的研究方向,但其在补丁验证过程中涉及到了大量的测试用例执行(比如百万量级的测试执行),因此时间开销问题严重.提出一种针对于统一化调试框架的加速技术(AUDE),该技术通过减少对缺陷定位效果无提升的测试执行,以提升统一化调试的效率.具体来说,AUDE首先通过马尔可夫链蒙特卡洛采样方法构建补丁执行的初始序列,随后在补丁执行过程中将已执行的补丁信息作为反馈信息,自适应性地估计每一个未执行补丁可能提供有效反馈信息的概率.在广泛使用的数据集Defects4J上对该技术进行了验证,发现AUDE在显著加速ProFL的同时,并没有降低其在缺...     

基于动态相似度的错误定位优先排序方法

在软件测试中,错误定位优先排序通过优化测试用例的执行次序来提高错误定位的效果,并将检测错误和定位错误相结合,以降低测试成本。 提出了一种基于动态相似度的错误定位优先排序方法,在相似度计算中,引入了语句怀疑度,提高了相似度计算的有效性以及错误定位的准确度;同时分析并验证了不同测试用例优先排序算法对后续定位错误的影响。在6个C基准程序上,针对3种广泛采用的测试用例优先排序算法和2种错误定位技术进行了实验,结果表明提出的方法能提高错误定位的准确度和效率。     

HSFal: Effective fault localization using hybrid spectrum of full slices and execution slices

Most of the existing fault localization approaches use execution coverage of test cases to isolate the suspicious codes that likely contain faults. Program slicing can extract the dependencies of program entities with respect to a specific criterion. Therefore this technique is expected to have a beneficial effect on fault localization. In this paper, we propose a novel approach using a hybrid spectrum of full slices and execution slices to improve the effectiveness of fault localization. In particular, our approach firstly computes full slices of failed test cases and execution slices of passed test cases respectively. Secondly it constructs the hybrid spectrum by intersecting full slices and execution slices. Finally it computes the suspiciousness of each statement in the hybrid slice spectrum and generates a fault location report with descending suspiciousness of each statement. We also implement our proposed approach in our prototype tool HSFal by Java programming language. To verify the effectiveness of our approach, we performed an empirical study by the prototype on several widely used open source programs. Our approach is compared with eight representative coverage-based and slice-based fault localization approaches. Final experimental results show that our proposed approach is more effective in fault localization than other compared approaches, and can reduce almost 2.98–31.79% of the average cost of examined code significantly.     

Slice-based statistical fault localization

Recent techniques for fault localization statistically analyze coverage information of a set of test runs to measure the correlations between program entities and program failures. However, coverage information cannot identify those program entities whose execution affects the output and therefore weakens the aforementioned correlations. This paper proposes a slice-based statistical fault localization approach to address this problem. Our approach utilizes program slices of a set of test runs to capture the influence of a program entity's execution on the output, and uses statistical analysis to measure the suspiciousness of each program entity being faulty. In addition, this paper presents a new slicing approach called approximate dynamic backward slice to balance the size and accuracy of a slice, and applies this slice to our statistical approach. We use two standard benchmarks and three real-life UNIX utility programs as our subjects, and compare our approach with a sufficient number of fault localization techniques. The experimental results show that our approach can significantly improve the effectiveness of fault localization.