基于SIEMENS STEP7的卸料小车自动对位控制方案

可移动卸料小车主要用于散状物料处理、矿山、堆场、冶金工厂以及港口设备中,它将物料卸在不同的料棚或仓位之中。本文以唐山中厚板厂烧结配料仓卸料小车为例,介绍了卸料小车的工艺要求、自动对位控制系统方案并给出了主要控制软件。     

基于SOA的多协议即时消息系统

目前解决即时消息互联互通的方案主要有网关策略与多协议客户端策略。采用多协议客户端策略的开源软件Pidgin在一定程度上解决了互联互通问题,但它存在着可修改性弱、可扩展性差、部署难的缺点,限制了其实用功能。基于SOA,对Pidgin软件进行了重新整合,从而构建一个开放、易修改、易扩展和易部署的多协议即时消息系统。     

软件维护实践中的过程控制

主要探讨的是,在永久项目组这种开发组织模式下,由软件的非直接开发人员承担软件维护工作时,需要进行的管理活动。目标是,找到方法,有效避免因这种情况下固有的缺陷而导致的软件加剧退化现象,还要避免由于软件本身缺陷对非直接开发人员的培养设置障碍。     

软件开发流程方法探析

首先对国内外软件开发理论的应用现状作了比较,然后重点分析软件系统开发流程,综合分析比较软件开发流程的各个阶段.     

平流层飞艇热性能分析软件开发

为减少平流层飞艇的设计周期和设计试验经费,清晰准确地反映出平流层飞艇空间轨迹和温度场分布以及各种不同因素对轨迹和温度场分布的影响,自主开发了平流层飞艇热性能分析软件.通过建立平流层飞艇运动和热特性模型,利用VC++编程环境,对平流层飞艇在不同输入状态下的上升、浮空、下降过程中的轨迹和温度进行数值仿真,并利用图形显示界面进行显示.软件设计包括参数输入、中间计算和性能分析三个部分.软件将输出结果存储到ACCESS数据库中,增强了数据的可读性和交互性.从软件的运行结果反映出该软件涵盖面广、数据可靠、操作方便、界面生动等特点,达到预期设计目标.     

基于可复用构件的系务管理信息系统的设计与实现

从理论研究的角度介绍了软件复用的基本概念和主要的构件技术,在此基础上重点讨论了基于可复用构件技术的软件复用,阐述了在系统开发中利用构件思想及应用的可行性,并对系务管理系统进行分析和可行性研究,在开发系务管理系统时遇到的有关软件复用方面的问题、解决方法以及实现策略.     

基于嵌入式Linux的RF手持设备红外通讯的设计

本文从硬件及软件两个方面介绍了TagmasterAB公司的RF手持设备的红外通讯的实现。硬件方面,结合AT91RM9200对红外接口的支持以及HSDL3602的硬件特性,给出了一个设计方案;软件方面,结合硬件特性,从驱动出发,分别讨论了上位机和下位机应用程序的实现过程。最后给出了该方案的设计结果。     

图档管理系统软件在图书管理中的应用

该文介绍了图档管理系统软件在图书管理中的应用,通过软件技术、图档管理概述、图书分类树设计、建立数据库,解决了图档管理系统软件在图书管理中的应用的一系列难题,并对某勘察设计院全部的图书进行管理。实现了图书档案一体化管理,节约投入资金,毋需另外购买专门的图书管理软件。     

Novel Metrics for Mutation Analysis

A measure of the “goodness” or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault detection ratio of the system. Because it is difficult to identify the system’s potential flaws precisely. In mutation testing, the Fault Detection Ratio (FDR) metric is currently used to express the adequacy of a test suite. However, there are some issues with this metric. If both test suites have the same defect detection rate, the smaller of the two tests is preferred. The test case (TC) is affected by the same issue. The smaller two test cases with identical performance are assumed to have superior performance. Another difficulty involves time. The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic. Our study developed three metrics to address these issues: , , and In this context, most used test generation tools were examined and tested using the developed metrics. Thanks to the metrics we have developed, the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.     

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.