基于双轨迹差异分析法的软件故障定位

软件运行监控器监测出故障之后，软件故障定位非常困难。该文提出了双轨迹差异分析法，根据成功的运行（run）和含有故障的运行之间的差异来进行故障定位。它采用程序谱来抽象表达程序执行轨迹，按照编辑距离度量来选取和含有故障的运行最近邻居的成功运行。通过序列间的最长共同子序列和最大稳定子序列集的计算，最终得到导致成功运行和失效运行之间差异的可疑故障语句集，并把它作为故障原因。经实验验证，该方法大大减少了故障定位中代码审查的范围。     

基于代码检测的软件故障定位方法

针对现有软件故障定位方法的缺陷，提出了一种基于代码检测的软件故障定位方法，用嵌入式模块获取软件发生故障时的模块运行序列，分析出软件故障可疑模块集及其故障系数，在此基础上对故障模块进行代码的分类检测，根据上述过程中得到的结果进行综合分析运算，得出软件故障的可疑代码集和故障系数，采用代码分析辅助工具进行排查，定位故障。该方法已成功应用于软件密集型系统的故障诊断，能快速有效地实现软件故障定位。     

基于时间序列数据挖掘的故障检测方法

为了有效地检测发动机试车实验中性能参数发生的异常,提出一种基于时间序列数据挖掘的发动机故障检测方法。通过基于形态特征的时间序列特征表示方法,将发动机参数时 间序列转化为符号序列,再根据符号语义对发动机参数序列实现稳态特征和过渡态特征识别。同时,根据稳态序列的数据特征,利用基于统计特征的时间序列相似性度量结合最不相似模式发现方法实现发动机的故障检测。数值实验结果表明,与传统方法相比,本文方法能够有效地对发动机性能参数进行故障检测,并且具有较强的鲁棒性。     

基于运行序列的软件故障诊断方法

在软件失效机理分析的基础上,提出了基于运行序列的软件故障诊断方法。该方法根据最近邻思想,采用编辑距离在大量正常运行中搜索故障运行的最近邻,利用故障运行序列与最近邻序列的对比差异生成程序可疑部分报告,并给出了报告的评价函数。最后设计试验验证了该方法。     

基于FSM的测试序列生成方法研究

为了降低UIO序列方法的测试序列长度,通过研究现有的测试序列生成方法,将可逆序列引入到测试序列的生成算法中,将其作为所有转移和状态的连接序列,并利用中国农村邮递员问题的解法构造一条最短遍历路径,使得各个状态的UIO序列之间的重复部分达到最大,测试序列的整体长度被缩短。对测试序列的实验结果表明,算法能够有效降低测试序列的长度。     

WSN中基于端到端的贪婪故障定位算法

为了维持无线传感器网络的正常运行,所有的故障链路需要被精确定位。将该问题转换为基于端到端的数据引导,以减少主动监测次数为目的的最优监测序列的问题。提出了通过拓扑拆分得到故障子图,并通过子图的概率集进一步计算节省主动探测次数的基于节点监测多条链路的启发式贪婪算法NTHG(node testing using heuristic greedy)。仿真结果表明仅需要监测小部分的节点,就可以定位网络中所有的故障链路。与该问题最新的解决算法LTHG(link testing using heristic greedy)相比,新算法需要更少的监测次数和平均CPU耗时,从而很好地降低了网络能耗,缩短了故障定位耗时。     

一种改进的基于交叉矩阵统计的软件故障定位技术

故障定位是软件调试过程中一项耗时耗力的工作,而自动故障定位技术能够很好地与自动测试技术相结合,对于提高软件调试效率具有重要的现实意义。提出了一种改进的基于交叉矩阵统计的软件故障定位技术。该方法在故障定位前先对所有的成功执行轨迹序列和失败执行轨迹序列进行聚类约减,以消除执行轨迹冗余;然后将消除冗余后的执行轨迹存储到交叉矩阵中;最后通过Crosstab算法计算出各语句的可疑度并对语句进行可疑度排序,进而产生故障报告。在西门子测试程序集上做了执行轨迹聚类约减前后的性能对比实验,实验结果验证了本文方法的有效性。     

基于单片机的配电线路故障检测方法研究

中低压配电线路中的接地方式多为中性点非有效接地,配电线路时常发生短路故障,故障多会发生在母线及多条出线中。为了识别判断母线多条出线中的故障线路,本文采用单片机作为检测工具,对配电线路的运行状态进行实时监控,通过显示器的示数变化情况判断哪条线路发生故障。在Proteus ISIS7 Professional软件中搭建模型,在MPLAB IDE软件中编写程序,将程序写入单片机,通过显示器的示数变化,发现故障线路所在,从而实现选线功能。实验结果证明了该方法的可行性。     

一种基于适应性测试的组合逻辑电路故障诊断方法

组合逻辑电路故障诊断主要包括：故障检测,故障定位,故障识别。其中,故障定位对故障排除和电路改进最为重要。在故障定位过程中确定电路定位测试集,生成电路测试码或测试序列是关键。只要得到定位测试集,按一定顺序对电路施加测试激励,根据电路响应就可以达到故障定位的目地。本文介绍一种基于适应性测试生成检测序列的方法。     

基于趋势特征表示的shapelet分类方法

Shapelet是一种具有辨识性的时间序列子序列,通过识别局部特征达到对时间序列准确分类的目的。原始shapelet发现算法效率较低,大量工作关注于提高shapelet发现的效率。然而,对于带有趋势变化的时间序列,采用典型的时间序列表示方法进行shapelet发现,容易造成序列中趋势信息的丢失。为了解决时间序列趋势信息丢失的问题,提出一种基于趋势特征的多样化top-k shapelet分类方法：首先采用趋势特征符号化方法对时间序列的趋势信息进行表示;然后针对序列的趋势特征符号获取shapelet候选集合;最后通过引入多样化top-k查询算法从候选集中选取k个最具代表性的shapelets。在时间序列的分类实验中,与传统分类算法相比,所提方法在11个数据集上的分类准确率均有提升;与FastShapelet算法相比,提升了运行效率,缩短了算法的运行时间,并在趋势信息明显的数据上效果显著。结果表明,所提方法能有效提高时间序列的分类准确率,提升算法运行效率。     

星载微波探测仪软件时序表化的设计方法

基于一个实际的星载微波探测仪软件项目,介绍了软件时序表化的设计方法.采用该方法实现的软件,其运行过程就是查询时序表的过程,主要特点是软件结构简单、模块功能和结构耦合松散、运行效率高、易更改和测试.这些特点在星载微波探测仪的设计、测试和定标过程中得到了充分的验证,设计方法可为其他航天软件的研制提供参考.     

Teradata University Network — Ein Portal zur Unterstützung der Lehre in den Bereichen Business Intelligence, Data Warehousing und Datenbanken

Business intelligence and information logistics have become a part of teaching curricula in recent years due to their importance for companies and the request for adequately trained graduates. Since these fields are characterized by a high amount of software and methodology innovations, teaching materials and teaching aids require constant attention. Teradata, a division of NCR Corp., has teamed up with lecturers and researchers to build and run a portal to support teaching business intelligence and information logistics. This article describes how faculty can use the Teradata University Network to prepare and run courses by reusing teaching materials and running state-of-the-art, commercial software provided in an ASP model.     

Shimba—an environment for reverse engineering Java software systems

Shimba is a reverse engineering environment to support the understanding of Java software systems. Shimba integrates the Rigi and SCED tools to analyze and visualize the static and dynamic aspects of a subject system. The static software artifacts and their dependencies are extracted from Java byte code and viewed as directed graphs using the Rigi reverse engineering environment. The run‐time information is generated by running the target software under a customized SDK debugger. The generated information is viewed as sequence diagrams using the SCED tool. In SCED, statechart diagrams can be synthesized automatically from sequence diagrams, allowing the user to investigate the overall run‐time behavior of objects in the target system. Shimba provides facilities to manage the different diagrams and to trace artifacts and relations across views. In Shimba, SCED sequence diagrams are used to slice the static dependency graphs produced by Rigi. In turn, Rigi graphs are used to guide the generation of SCED sequence diagrams and to raise their level of abstraction. We show how the information exchange among the views enables goal‐driven reverse engineering tasks and aids the overall understanding of the target software system. The FUJABA software system serves as a case study to illustrate and validate the Shimba reverse engineering environment. Copyright © 2001 John Wiley & Sons, Ltd.     

基于时空相关性的数字电影放映场次预测

为提高数字电影流动放映中各院线放映场次的预测精度,基于院线放映场次的时空相关性,提出一种最邻近法和时空序列相结合的预测方法：通过最邻近法选取与待预测院线放映最相关的院线作为预测辅助院线;通过构造神经网络的时空序列将这些相关院线放映场次的时、空特征结合起来,产生更精确的预测模型。实验对比分析了此方法与传统时间序列预测方法的预测结果,证明了该方法具有更高的预测精度。     

PIM-Align: A Processing-in-Memory Architecture for FM-Index Search Algorithm

Genomic sequence alignment is the most critical and time-consuming step in genomic analysis.Alignment al-gorithms generally follow a seed-and-extend model.Acceleration of the extension phase for sequence alignment has been well explored in computing-centric architectures on field-programmable gate array(FPGA),application-specific integrated circuit(ASIC),and graphics processing unit(GPU)(e.g.,the Smith-Waterman algorithm).Compared with the extension phase,the seeding phase is more critical and essential.However,the seeding phase is bounded by memory,i.e.,fine-grained random memory access and limited parallelism on conventional system.In this paper,we argue that the processing-in-memory(PIM)concept could be a viable solution to address these problems.This paper describes"PIM-Align"—an application-driven near-data processing architecture for sequence alignment.In order to achieve memory-capacity proportional performance by taking advantage of 3D-stacked dynamic random access memory(DRAM)technology,we propose a lightweight message mechanism between different memory partitions,and a specialized hardware prefetcher for memory access patterns of se-quence alignment.Our evaluation shows that the proposed architecture can achieve 20x and 1820x speedup when compared with the best available ASIC implementation and the software running on 32-thread CPU,respectively.     

Security implications of running windows software on a Linux system using Wine: a malware analysis study

Linux is considered to be less prone to malware compared to other operating systems, and as a result Linux users rarely run anti-malware. However, many popular software applications released on other platforms cannot run natively on Linux. Wine is a popular compatibility layer for running Windows programs on Linux. The level of security risk that Wine poses to Linux users is largely undocumented. This project was conducted to assess the security implications of using Wine, and to determine if any specific types of malware or malware behavior have a significant effect on the malware being successful in Wine. Dynamic analysis (both automated and manual) was applied to 30 malware samples both in a Windows environment and Linux environment running Wine. Behavior analyzed included file system, registry, and network access, and the spawning of processes, and services. The behavior was compared to determine malware success in Wine. The study results provide evidence that Wine can pose serious security implications when used to run Windows software in a Linux environment. Five samples of Windows malware were run successfully through Wine on a Linux system. No significant relationships were discovered between the success of the malware and its high-level behavior or malware type. However, certain API calls could not be recreated in a Linux environment, and led to failure of malware to execute via Wine. This suggests that particular malware samples that utilize these API calls will never run completely successfully in a Linux environment. As a consequence, the success of some samples can be determined from observing the API calls when run within a Windows environment.

     

k最近邻流序列算法对异常流检测的优化研究

通过时空异常流检测技术可以发现城市交通数据中的异常交通特征。与时间序列中单个异常流检测采用的方法不同,提出了从流序列中检测异常流分布的k最近邻流序列算法（kNNFS）。算法首先为每个位置测定每个时间区间内的单个流观测值;随后计算单个流的观测频率来构建每个位置处每个时间区间的流分布概率库;最后由阈值判定使用KL散度计算的新的流分布概率与其k最近邻之间的距离是否为异常值,距离值小于阈值则更新入流分布概率库,否则为异常的流分布。仿真分析表明,对比DPMM算法和SETMADA算法,kNNFS算法在检测精度和算法运行时间方面均有优化提升。     

基于软件行为轨迹的可信性评价模型

软件动态可信性评价已经成为信息安全领域研究的一个热点问题.为了提高评价的精确性,在充分考虑了软件的运行流程和运行背景的基础上,提出了基于软件行为轨迹的可信性评价模型(CEMSBT).该模型引入软件行为轨迹描述软件行为,软件行为轨迹由运行轨迹和功能轨迹构成,运行轨迹表示软件运行时的有序操作,表征为有序的检查点向量;功能轨迹则由能够表征软件功能的一系列场景来刻画.为了减少可信性评价的时间和空间开销,给出了软件行为轨迹的化简规则.模型应用检查点的标识评价规则和场景评价规则对实际的软件行为进行评价.考虑到分支给程序带来的随机性很可能被入侵者利用,分支处的检查很必要.模型通过场景确定分支的走向,从而降低了分支处异常情况的漏报率.仿真实验表明CEMSBT具有较高的精确性和效率.     

Fast exact nearest patch matching for patch-based image editing and processing

This paper presents an efficient exact nearest patch matching algorithm which can accurately find the most similar patch-pairs between source and target image. Traditional match matching algorithms treat each pixel/patch as an independent sample and build a hierarchical data structure, such as kd-tree, to accelerate nearest patch finding. However, most of these approaches can only find approximate nearest patch and do not explore the sequential overlap between patches. Hence, they are neither accurate in quality nor optimal in speed. By eliminating redundant similarity computation of sequential overlap between patches, our method finds the exact nearest patch in brute-force style but reduces its running time complexity to be linear on the patch size. Furthermore, relying on recent multicore graphics hardware, our method can be further accelerated by at least an order of magnitude (≥10×). This greatly improves performance and ensures that our method can be efficiently applied in an interactive editing framework for moderate-sized image even video. To our knowledge, this approach is the fastest exact nearest patch matching method for high-dimensional patch and also its extra memory requirement is minimal. Comparisons with the popular nearest patch matching methods in the experimental results demonstrate the merits of our algorithm.