When Disaster Strikes

The importance of securing data against the threat of natural disasters i s something most MIS managers don't realize until it's too late. This column describes how two fires and an earthquake adversely affected several companies that were unprepared, or only partially prepared, for such disasters. It also discusses disaster recovery measures some MIS executives believe their companies should strengthen not only to protect the data center but to reduce their employees risk of losing valuable informa- tion from office files.     

国际水电总承包项目关键风险识别案例分析

文章针对国际水电工程总承包项目识别出的关键风险因素,通过几个国际项目,进行案例分析,佐证其风险因素的存在,从而得出国际水电工程总承包项目的关键风险源,为国际水电工程总承包项目风险管理提供了可操作性及指导性的经验。     

基于CBR的信息化装备故障案例推理复用技术研究

针对信息化装备技术保障要求,在对信息化装备故障案例特点进行分析的基础上,运用CBR理论解决信息化装备故障诊断的实际问题,提出了信息化装备故障案例推理复用策略,为构造实用的信息化装备故障诊断专家系统提供支持,对加强信息化装备技术保障具有积极的意义.     

软件可靠性测试用例的复用度量

针对目前基于复用的软件可靠性测试缺少对测试用例复用度量的现实问题,在构建测试用例复用模型的基础上,首先应用特征分析法求解测试用例各复用资源的权重,然后再分别计算它们在测试中的复用度,最后求解得到测试用例的平均复用度。从定量的角度解决了测试用例的复用度量问题。     

一种基于改进遗传算法的面向路径测试用例自动生成方法

介绍了基于改进遗传算法测试用例生成的核心算法,针对软件测试用例生成问题的特点对传统遗传算法的遗传算子进行了改进,实验获得了较好的效果。     

基于子图同构与事例匹配的装配体局部结构相似性分析

基于事例推理是解决面向装配的设计和装配工艺规划问题的有效方法,装配体检索是应用此方法的关键步骤.为此提出一种基于子图同构与事例匹配的装配体局部结构相似性分析方法.该方法将装配体零件间的装配关系用属性邻接图来表示,通过对装配体中联结关系的定性分类及装配体属性邻接图预处理规则的定义,利用Ullmann算法匹配出相似的装配体图(子图)结构,并给出了装配体局部结构相似度计算公式.最后通过实例验证了文中方法的有效性.     

模型检测在软件测试中的应用研究

以测试为基础的模型或规格是产生测试用例的一种很有潜力技术。在这种方法中,先要建立一个系统的规格或模型,再由规格或模型产生测试用例。文章对结合模型检测来进行测试的方法进行了研究,从整体上对测试进行考虑,不仅包含了对系统所希望具有的功能进行测试,还包括了对系统不应具有的功能进行测试。通过这种方法,我们可以进一步保证系统的正确性和可靠,大大降低人力和资源的开销,为进一步优化测试奠定了基础。     

高职计算机专业程序设计教学探讨

针对高职计算机专业程序设计的教学,提出了以市场需求为导向,技能培养为目标,结合市场的需求,采用项目驱动,以实用的案例进行教学。提高了学生的学习热情和程序设计能力,所学的知识具有实用性,能促进学生的就业。     

On the tractability of coloring semirandom graphs

As part of the efforts to understand the intricacies of the k-colorability problem, different distributions over k-colorable graphs have been analyzed. While the problem is notoriously hard (not even reasonably approximable) in the worst case, the average case (with respect to such distributions) often turns out to be “easy”. Semi-random models mediate between these two extremes and are more suitable to imitate “real-life” instances than purely random models. In this work we consider semi-random variants of the planted k-colorability distribution. This continues a line of research pursued by Coja-Oghlan, and by Krivelevich and Vilenchik. Our aim is to study a more general semi-random framework than those suggested so far. On the one hand we show that previous algorithmic techniques extend to our more general semi-random setting; on the other hand we give a hardness result, proving that a closely related semi-random model is intractable. Thus we provide some indication about which properties of the input distribution make the k-colorability problem hard.     

Distributing test cases more evenly in adaptive random testing

Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities.