杀毒软件 全面战争

金山毒霸终结测试,KV3000呼之欲出 11月18日,金山毒霸,这个以免费测试走入千机万窗的热情过客,以层出不穷的升级“刀法”定居热门下载榜的快刀好手,终于告别了长达一年半的襁褓。10月下旬,网络上出现了这样一段文字: 金山毒霸历时18个月的测试工作已接近尾声,金山公司于本周开始发放金山毒霸终极测试版,这是在金山毒霸正式版上市之前的最后一个测试版本。金山公司衷心感谢广大测试版用户对金山毒霸长期以来的支持。金山毒霸的成长,与各位用户的配合是分不开的。     

三合一的杀毒软件

KV3000在杀毒软件市场上一直占有比 较重要的地位,这次北京江民新科技术有限公司给我们送来了该软件的最新版5.0.108版,它不仅在查杀病毒功能的基础上增加了防黑客和硬盘救护的功能,而     

杀毒软件共存术

问题现象:如果在系统中安装金山毒霸2003、瑞星2003、KV3000杀毒王等多种杀毒软件会引起程序冲突,有时引起系统崩溃,怎样解决? 技术分析:由于各杀毒软件注册系统关键服务进程,导制系统资源溃乏,出现系统错误! 解决方法:这些最新版杀毒软件为保证杀毒效果,会将自己的多个进程注册为系统服务,供主程序调用,普通用户无法将其关闭,像金山毒霸、瑞星、KV新款都有此功能,如KV的系统服务进程名为KVSrv.exe,系统服务描述为     

杀毒软件使用技巧大集合

现在的个人电脑里，我相信除了安装操作系统之外，有一种软件是大家都会安装的，那就是杀毒软件。随着病毒的越来越猖狂，病毒数量的不断增多，病毒的不断传播，杀毒软件现在已经成为必备的软件之一出现在大家的电脑里了。     

冷血杀手的日记—记三款杀毒软件

某年某月某日,夜黑的让人害怕,正可谓是月黑风高杀人夜。郊外漆黑的树林里,正在掀起一场血雨腥风……哎呀！谁扔的臭鸡蛋啊……(小编语,想骗稿费呀)唉！以下挥泪略去精彩场景描写千余字,言归正传。想必大家一定对前一段时间的杀毒软件大战还记忆犹新吧,随着杀毒大战硝烟的渐渐散去,让我们来好好见识一下这次大战中的主角——三位对病毒无情剿杀的冷血杀手:金山毒霸2003,KV3000杀毒王和瑞星2003。     

Evaluating and selecting software packages: A review

Evaluating and selecting software packages that meet an organization’s requirements is a difficult software engineering process. Selection of a wrong software package can turn out to be costly and adversely affect business processes. The aim of this paper is to provide a basis to improve the process of evaluation and selection of the software packages. This paper reports a systematic review of papers published in journals and conference proceedings. The review investigates methodologies for selecting software packages, software evaluation techniques, software evaluation criteria, and systems that support decision makers in evaluating software packages. The key findings of the review are: (1) analytic hierarchy process has been widely used for evaluation of the software packages, (2) there is lack of a common list of generic software evaluation criteria and its meaning, and (3) there is need to develop a framework comprising of software selection methodology, evaluation technique, evaluation criteria, and system to assist decision makers in software selection.     

Trade-offs between productivity and quality in selecting software development practices

The authors report detailed data and analyses on productivity and quality from 29 Hewlett-Packard projects. While some software development models' characteristics affect performance negatively when considered alone, their impact disappears when considered in combination with other attributes. So, processes should be thought of as coherent systems of activities rather than as a series of individual practices that can be implemented piecemeal.     

航天型号软件生存周期模型选择指南

文中首先阐述了有关生存周期模型的基本概念,简要介绍4个已在国内外航天界得到成功应用的典型生存周期模型：瀑布模型、增量模型、进化模型和基于软件包的生存周期模型,分别给出了这4个生存周期模型的优点、缺点、适用情况以及各生存周期阶段的主要产品和里程碑评审,最后给出选择航天型号软件生存周期模型的基本原则。     

How to steer an embedded software project: tactics for selecting the software process model

Modern large new product developments (NPD) are typically characterized by many uncertainties and frequent changes. Often the embedded software development projects working on such products face many problems compared to traditional, placid project environments. One of the major project management decisions is then the selection of the project's software process model. An appropriate process model helps coping with the challenges, and prevents many potential project problems. On the other hand, an unsuitable process choice causes additional problems. This paper investigates the software process model selection in the context of large market-driven embedded software product development for new telecommunications equipment. Based on a quasi-formal comparison of publicly known software process models including modern agile methodologies, we propose a process model selection frame, which the project manager can use as a systematic guide for (re)choosing the project's process model. A novel feature of this comparative selection model is that we make the comparison against typical software project problem issues. Some real-life project case examples are examined against this model. The selection matrix expresses how different process models answer to different questions, and indeed there is not a single process model that would answer all the questions. On the contrary, some of the seeds to the project problems are in the process models themselves. However, being conscious of these problems and pitfalls when steering a project enables the project manager to master the situation.     

Predicting high-risk program modules by selecting the right software measurements

A timely detection of high-risk program modules in high-assurance software is critical for avoiding the high consequences of operational failures. While software risk can initiate from external sources, such as management or outsourcing, software quality is adversely affected when internal software risks are realized, such as improper practice of standard software processes or lack of a defined software quality infrastructure. Practitioners employ various techniques to identify and rectify high-risk or low-quality program modules. Effectiveness of detecting such modules is affected by the software measurements used, making feature selection an important step during software quality prediction. We use a wrapper-based feature ranking technique to select the optimal set of software metrics to build defect prediction models. We also address the adverse effects of class imbalance (very few low-quality modules compared to high-quality modules), a practical problem observed in high-assurance systems. Applying a data sampling technique followed by feature selection is a relatively unique contribution of our work. We present a comprehensive investigation on the impact of data sampling followed by attribute selection on the defect predictors built with imbalanced data. The case study data are obtained from several real-world high-assurance software projects. The key results are that attribute selection is more efficient when applied after data sampling, and defect prediction performance generally improves after applying data sampling and feature selection.     

Finding conclusion stability for selecting the best effort predictor in software effort estimation

Background: Conclusion Instability in software effort estimation (SEE) refers to the inconsistent results produced by a diversity of predictors using different datasets. This is largely due to the “ranking instability” problem, which is highly related to the evaluation criteria and the subset of the data being used. Aim: To determine stable rankings of different predictors. Method: 90 predictors are used with 20 datasets and evaluated using 7 performance measures, whose results are subject to Wilcoxon rank test (95 %). These results are called the “aggregate results”. The aggregate results are challenged by a sanity check, which focuses on a single error measure (MRE) and uses a newly developed evaluation algorithm called CLUSTER. These results are called the “specific results.” Results: Aggregate results show that: (1) It is now possible to draw stable conclusions about the relative performance of SEE predictors; (2) Regression trees or analogy-based methods are the best performers. The aggregate results are also confirmed by the specific results of the sanity check. Conclusion: This study offers means to address the conclusion instability issue in SEE, which is an important finding for empirical software engineering.