VB打印技术研究

提出两种技术可提高打印开发的速度和质量.通过程序自动识别窗体上文本框、标签和图片框等控件,并按以上控件对应打印程序进行输出,实现打印的自动化;介绍一种打印预览实用技术,可实现复杂格式的输出.     

基于VC 和ADO的燃气调压站管理系统开发

本文介绍了一个基于VisualC 6.0开发平台的管理系统数据库的开发过程,该系统采用ADO数据库访问技术.实现了数据录入、信息查询、分级管理、查看操作日志、添加、修改、删除记录及打印报表等功能,并给出了相关程序的代码.     

自动化进程日志取证分析工具设计

日志分析工作耗时且繁琐,尽管已有的日志分析工具提供了某些过滤或搜索功能,但它们并不具备任何原生的数据分析能力,也没有提供过滤大量合法进程活动的功能。本文基于Windows的PowerShell脚本,设计了一款能够自动化分析进程日志的工具。该工具弥补了已有日志分析工具的上述不足,能够帮助取证分析人员快速定位异常进程或程序。     

软件外包项目工作日志质量评估方法

工作日志是软件外包项目监控项目进展的一个重要手段,它由工作人员填写汇报项目进展. 工作日志的质量一定程度上体现了过程执行的质量, 但是由于其数量庞大内容琐碎, 很难依靠人工完成查看和评估. 现有的研究对日志质量的关注较少, 因此本文提出一个自动化评估日志质量的方法, 该方法利用词法分析、依存句法分析、LDA主题模型对历史日志数据进行分析和挖掘, 从结构、内容、主题相关性等方面选取质量特征, 通过专家小组标注的方法获得训练数据, 使用分类算法建立质量评估模型, 通过模型对日志质量进行自动化评估. 本文以一个国家核高基项目为案例, 实现了一个具有较高准确率的评估模型, 结果表明本文的方法能够合理的评估日志质量, 为外包单位提供有效的决策支持.     

基于日志数据的分布式软件系统故障诊断综述

基于日志数据的故障诊断指通过智能化手段分析系统运行时产生的日志数据以自动化地发现系统异常、诊断系统故障.随着智能运维（Artificial Intelligence for IT Operations,AIOps）的快速发展,该技术正成为学术界和工业界的研究热点.本文首先总结了基于日志数据的分布式软件系统故障诊断研究框架,然后就日志处理与特征提取、基于日志数据的异常检测、基于日志数据的故障预测和基于日志数据分析的故障根因诊断等关键技术对近年来国内外相关工作进行了深入地分析,最后以本文提出的研究框架为指导总结相关研究工作,并对未来研究可能面临的挑战进行了展望.     

基于二次滑动窗口机制的日志异常检测方法

针对日志异常检测的传统特征提取方法往往选取一定数量的日志进行特征提取,在程序并发和网络时延波动较大等导致日志顺序混乱的场景下,传统方法效果不够理想.本文提出一种基于二次滑动窗口机制的日志异常检测方法,首先基于正则表达式和日志解析方法提取出日志时间戳和模板信息,再先后两次采用滑动窗口方法获取特征提取的序列对象.其中初次滑...     

基于并行计算的海量日志分析系统实现

通过深入研究日志的类型和特点,设计并实现了一套基于并行计算的海量日志文件分析系统.该系统采用集群方式并行地收集日志文件,采用分布式文件系统存储,最终利用并行计算对日志进行分析处理.该系统实现了日志采集、分析的完全自动化处理,在系统部署之后能够有效地进行系统安全的维护、系统性能的优化、系统故障的排查.该系统结合云计算提高了日志分析的效率,解决了海量日志处理过程中存在的问题,为海量日志分析提供了一个完整有效的解决方案.     

灵活的多变体打印程序—BTDY

随着微机在办公室自动化中应用的推广和普及,用户对文件打印的字型要求也越来越高。要求它不仅能打印缩体汉字,而且更希望能印出放大的汉字.但是,目前国内微机上所配的汉字打印驱动程序一般印出的汉字最大只为标准型的4倍,并且字型选择也只有十几种。为了克服这一缺点,满足办公室自动化对文件打印的要求,我们在IBM—PC/XT 机上研制了一种适合 M2024打印机的灵活、多变体打印程序——BTDY,从而弥补了汉字打印驱动程序的不足,使个人计算机的汉字处理功能得到了进一步扩充。     

基于GUI的Android移动软件性能测试

移动应用软件已经拥有了数以千万计的用户群体.根据最新统计,Android手机以85.1%的市场占有份额,成为了最受欢迎的移动端设备.Android应用软件的快速开发,使得如何保证程序质量,成了难题.我们不仅要考虑程序的正确性,也应保证运行时的流畅性.现有的性能研究工作都基于传统的静态分析或者动态执行.对于Android程序,静态分析具有一定的局限性,而动态分析又忽略了APP执行时的遍历方式.因此,本文提出了基于GUI的Android自动化性能测试框架,将着重关注页面状态和APP状态的相关性.通过对页面的分析,聚合,尽可能遍历到APP的各个状态,然后从日志中,找出APP性能上的问题.本框架使用Java作为开发语言,搭建了Android移动软件自动化性能测试框架,并在开源社区F-Droid上随机抽取了移动应用软件进行实验.实验表明,该技术能更多的遍历APP的状态,发现APP在运行过程中出现的性能问题,取得良好的效果.     

打印机监控系统的设计与实现

针对如今企事业单位局域网内部打印机管理难的问题,提出了利用活动目录来实现打印机的管理。设计了基于活动目录的打印机监控系统。通过该系统,注册用户可以直接打印,无论是否成功均会以日志的形式被记录进数据库中。未注册用户无权打印文件。当系统检测到非法用户,将会自动删除打印任务,并记录打印日志。经过测试证明,该方案能实现对用户和打印任务进行有效的监控,为企业内部的打印机管理提供了方便、快捷的途径。     

Characterizing logging practices in Java-based open source software projects – a replication study in Apache Software Foundation

Log messages, which are generated by the debug statements that developers insert into the code at runtime, contain rich information about the runtime behavior of software systems. Log messages are used widely for system monitoring, problem diagnoses and legal compliances. Yuan et al. performed the first empirical study on the logging practices in open source software systems. They studied the development history of four C/C++ server-side projects and derived ten interesting findings. In this paper, we have performed a replication study in order to assess whether their findings would be applicable to Java projects in Apache Software Foundations. We examined 21 different Java-based open source projects from three different categories: server-side, client-side and supporting-component. Similar to the original study, our results show that all projects contain logging code, which is actively maintained. However, contrary to the original study, bug reports containing log messages take a longer time to resolve than bug reports without log messages. A significantly higher portion of log updates are for enhancing the quality of logs (e.g., formatting & style changes and spelling/grammar fixes) rather than co-changes with feature implementations (e.g., updating variable names).     

A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons

The estimation of software development effort has been centralized mostly on the accuracy of estimates through dealing with heterogeneous datasets regardless of the fact that the software projects are inherently complex and uncertain. In particular, Analogy Based Estimation (ABE), as a widely accepted estimation method, suffers a great deal from the problem of inconsistent and non-normal datasets because it is a comparison-based method and the quality of comparisons strongly depends on the consistency of projects. In order to overcome this problem, prior studies have suggested the use of weighting methods, outlier elimination techniques and various types of soft computing methods. However the proposed methods have reduced the complexity and uncertainty of projects, the results are not still convincing and the methods are limited to a special domain of software projects, which causes the generalization of methods to be impossible. Localization of comparison and weighting processes through clustering of projects is the main idea behind this paper. A hybrid model is proposed in which the software projects are divided into several clusters based on key attributes (development type, organization type and development platform). A combination of ABE and Particle Swarm Optimization (PSO) algorithm is used to design a weighting system in which the project attributes of different clusters are given different weights. Instead of comparing a new project with all the historical projects, it is only compared with the projects located in the related clusters based on the common attributes. The proposed method was evaluated through three real datasets that include a total of 505 software projects. The performance of the proposed model was compared with other well-known estimation methods and the promising results showed that the proposed localization can considerably improve the accuracy of estimates. Besides the increase in accuracy, the results also certified that the proposed method is flexible enough to be used in a wide range of software projects.     

基于机器学习的日志函数自动识别方法

随着软件规模的不断增长,日志在故障检测中发挥着愈加重要的作用。然而,目前软件日志缺乏统一标准,常受开发人员个人习惯影响,为大规模系统中日志的自动化分析带来了挑战。其中,日志函数的识别作为日志分析的前提条件,对分析结果有着直接影响。提出了一种基于机器学习的方法以支持日志自动识别。通过系统分析广泛使用的大规模开源软件,总结出日志函数编写的主要形式,并提取不同形式间的共性特征,进而基于机器学习实现了自动日志识别工具iLog。实验显示,使用iLog识别的日志函数能力平均为使用特定关键字的76倍,十折交叉验证得到iLog的分析结果的F-Score为0.93。     

Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images

The performance of supervised classification algorithms is highly dependent on the quality of training data. Ambiguous training patterns may misguide the classifier leading to poor classification performance. Further, the manual exploration of class labels is an expensive and time consuming process. An automatic method is needed to identify noisy samples in the training data to improve the decision making process. This article presents a new classification technique by combining an unsupervised learning technique (i.e. fuzzy c-means clustering (FCM)) and supervised learning technique (i.e. back-propagation artificial neural network (BPANN)) to categorize benign and malignant tumors in breast ultrasound images. Unsupervised learning is employed to identify ambiguous examples in the training data. Experiments were conducted on 178 B-mode breast ultrasound images containing 88 benign and 90 malignant cases on MATLAB® software platform. A total of 457 features were extracted from ultrasound images followed by feature selection to determine the most significant features. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and Mathew's correlation coefficient (MCC) were used to access the performance of different classifiers. The result shows that the proposed approach achieves classification accuracy of 95.862% when all the 457 features were used for classification. However, the accuracy is reduced to 94.138% when only 19 most relevant features selected by multi-criterion feature selection approach were used for classification. The results were discussed in light of some recently reported studies. The empirical results suggest that eliminating doubtful training examples can improve the decision making performance of expert systems. The proposed approach show promising results and need further evaluation in other applications of expert and intelligent systems.     

The role of expert systems in producing log interpretation software

Abstract: Log interpretation science is a controversial and rapidly changing domain. Designing interpretation models is a highly experimental process which involves trials with a computer program as an integral part of the design. Therefore conventional software engineering techniques, which require a complete specification of the problem before the program is written, are often not applicable or fail to produce high quality software. The development of expert systems has provided the techniques, tools, and capabilities to let us seek alternate methods to produce log interpretation software: exploratory programming environments and automatic programming systems. An exploratory programming environment combines the power of interactive graphics and programming tools to merge the design and programming tasks into a single process where model and program develop together. An automatic programming system will embody the knowledge of the programming process and of some log interpretation heuristics to produce log processing programs from interactive specifications expressed in familiar terms. These facilities will allow log interpretation model designers, who are non-computer specialists, to produce high quality software as the end result of a model design.     

数值稳定性相关漏洞隐患的自动化检测方法

安全漏洞检测是保障软件安全性的重要手段.随着互联网的发展,黑客的攻击手段日趋多样化,且攻击技术不断翻新,使软件安全受到了新的威胁.本文描述了当前软件中实际存在的一种新类型的安全漏洞隐患,我们称之为数值稳定性相关的安全漏洞隐患.由于黑客可以利用该类漏洞绕过现有的防护措施,且已有的数值稳定性分析方法很难检测到该类漏洞的存在,因而这一新类型的漏洞隐患十分危险.面对这一挑战,本文首先从数值稳定性引起软件行为改变的角度定义了数值稳定性相关的安全漏洞隐患,并给出了对应的自动化检测方法.该方法基于动静态相结合的程序分析与符号执行技术,通过数值变量符号式提取、静态攻击流程分析、以及高精度动态攻击验证三个步骤,来检测和分析软件中可能存在的数值稳定性相关安全漏洞.我们在业界多个著名开源软件上进行了实例研究,实验结果表明,本文方法能够有效检测到实际软件中真实存在的数值稳定性相关漏洞隐患.     

一致持久的带外虚拟化系统

存储虚拟化能够全面提升存储区域网络的服务质量,而带外虚拟化与带内虚拟化相比具有性能高和扩展性好等优点．提出了运用按序操作、REDO日志以及日志完整性鉴别,共同保证带外虚拟化跨越系统崩溃或断电事故后仍旧可用;设计了一种基于关系模型的磁盘上虚拟化元数据组织方式,它具有可读性好和容易修改的优点;给出了通过分析磁盘分区表兼容典型遗留存储系统的有效方法,它具有庞大遗留系统零切割时间的特点．基于上述关键技术实现的原型系统,在典型实验中表现了很好的性能．     

基于政策自组织系统的软件agent体系结构及其实现

当前自组织系统软件工程面临的一个重要挑战,就是如何设计适宜的个体交互行为来满足自组织系统的宏观涌现需求。针对此问题,提出了一种基于政策的自组织多agent系统的开发方法,此方法通过政策调节引导agent的行为,以期在系统层面得到用户所需求的宏观涌现结果。开发这类系统的核心问题是如何构造系统中的软件agent,使得agent能够感知、理解系统政策,并在遵循政策的前提下实现行为的自主决策。提出了一种基于政策自组织多agent系统的软件agent体系结构,并基于该体系结构设计了运行机制及行为决策算法。通过软件方式实现了一个基于政策的自组织多agent系统开发平台原型,并通过案例实现说明了体系结构、运行机制的有效性。     

Functional networks as a novel data mining paradigm in forecasting software development efforts

This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regression, and case-based reasoning (CBR). Unfortunately, such forecasting models have numerous of drawbacks, namely, their inability to deal with uncertainties and imprecision present in software projects early in the development life-cycle. The main benefit of this study is to utilize both function points and development environments of recent software development cases prominent, which have high impact on the success of software development projects. Both implementation and learning process are briefly proposed. We investigate the efficiency of the new framework for predicting the software development efforts using both simulation and COCOMO real-life databases. Prediction accuracy of the functional networks framework is evaluated and compared with the commonly used regression and neural networks-based models. The results show that the new intelligence paradigm predicts the required efforts of the initial stage of software development with reliable performance and outperforms both regression and neural networks-based models.