省计算机等级考试上报数据的自动生成系统实现

作为浙江省重要的高校计算机等级考试,每年都要由承办考试的相关单位进行考生缺考数据的上报,在每年的上报数据中,都会或多或少地出现误报考生数据的情况,原因是采用了人工输入实际缺考考生数据的方法.为此,经过不断的研究和检验,用VBA编程实现了计算机自动生成上报数据.实践表明,计算机自动生成上报数据的过程是可行的,也能正确形成上报的缺考库.     

全国高校计算机等级考试报名方法改进的探讨——在线报名数据的格式转换

本文主要研究根据全国高校计算机等级考试（CCT即College Computer Test）报名的需要,开发了在线考试报名系统后,如何使此系统的报名信息转换成CCT报名软件能识别的格式,以便导入到该报名软件。实践证明,此功能实现了数据集成性录入,减少了录入数据错误,大大提高了报名效率。     

计算机等级考试管理系统的设计与实现

本文首先提出了等级考试在管理上的一些问题,然后给出了解决这些问题的方案,并详细描述方案中各环节的实施角色、过程和其他要点;其次列出了管理系统的关键表以及核心代码。     

计算机等级考试系统的设计与实现

本文论述了福建省高校计算机等级考试系统（ficges)的网络构成，主要功能和软件设计。最后介绍了组卷算法的设计与实现。实践证明，该算法是有效的。     

数据访问在计算机考试自动阅卷子系统中的应用

通过计算机考试软件阅卷子系统的开发实践，介绍了两种数据源的访问技术：ODBC数据访问技术和非Foxpro环境下的Foxpro操作技术。     

基于云的计算机等级考试架构

传统的计算机等级考试安装系统和服务器花费大量人力物力和时间,部署及更改系统相对困难,并且每个考场均需安装一台服务器,为防止系统崩溃和软件故障还需备份一台服务器,服务器利用率较低;目前国内数据中心及云计算平台的兴起,采用虚拟化的技术使得在物理服务器上构架虚拟服务器和桌面成为了现实,采用云计算平台的计算机等级考试系统可以改变传统考试系统的多种弊端,节约资源,提高硬件的使用率和考试系统的安全性、稳定性.     

论计算机等级考试的利与弊

计算机等级考试就为社会提供了这样一个相对统一、公正和客观的计算机应用能力考核标准,现在已成为大学生求职就业的重要凭证之一,然而笔者在多年的计算机基础教学中发现,在大学生中开展计算机等级考试有利也有弊,本文就对计算机等级考试的利与弊谈谈自己的想法。     

浅议计算机等级考试对计算机基础教育的影响

计算机基础教育是面向广大大中专学生的普及性教育,笔者针对所在学校学生参加计算机等级考试的具体情况,分析了计算机等级考试对计算机基础教育的影响,并提出了几点建议.     

全国计算机等级考试工作经验谈

随着计算机知识在我国的不断普及，各行各业都在广泛地使用计算机，可以说计算机已经渗透到人类生产和生活的几乎每个领域，它的发展是现代化科学技术的标志。计算机知识已经成为当代知识结构中不可缺少的一个重要组成部分，面对汹涌的信息化浪潮，社会上有愈来愈多的人迫切要求掌握计算机知识以满足工作中的需求，国家教育部考试中心推出了面向社会了“全国计算机等级考试”，其考试的目的是力求为社会提供了一个统一、公正、客观的计算机知识和应用技能考核标准，供社会上各用人单位作为招聘人才和上岗资格的重要依据。在2002年下半年全国…     

Constraint-based automatic test data generation

A novel technique for automatically generating test data is presented. The technique is based on mutation analysis and creates test data that approximate relative adequacy. It is a fault-based technique that uses algebraic constraints to describe test cases designed to find particular types of faults. A set of tools (collectively called Godzilla) that automatically generates constraints and solves them to create test cases for unit and module testing has been implemented. Godzilla has been integrated with the Mothra testing system and has been used as an effective way to generate test data that kill program mutants. The authors present an initial list of constraints and discuss some of the problems that have been solved to develop the complete implementation of the technique     

Cloud-based automatic test data generation framework

Designing test cases is one of the most crucial activities in software testing process. Manual test case design might result in inadequate testing outputs due to lack of expertise and/or skill requirements. This article delivers automatic test data generation framework by effectively utilizing soft computing technique with Apache Hadoop MapReduce as the parallelization framework. We have evaluated and analyzed statistically our proposed framework using real world open source libraries. The experimental results conducted on Hadoop cluster with ten nodes are effective and our framework significantly outperforms other existing cloud-based testing models.     

An integrated automatic test data generation system

The Godzilla automatic test data generator is an integrated collection of tools that implements a relatively new test data generation method—constraint-based testing—that is based on mutation analysis. Constraint-based testing integrates mutation analysis with several other testing techniques, including statement coverage, branch coverage, domain perturbation, and symbolic evaluation. Because Godzilla uses a rule-based approach to generate test data, it is easily extendible to allow new testing techniques to be integrated into the current system. This article describes the system that has been built to implement constraint-based testing. Godzilla's design emphasizes orthogonality and modularity, allowing relatively easy extensions. Godzilla's internal structure and algorithms are described with emphasis on internal structures of the system and the engineering problems that were solved during the implementation.Parts of this research were supported by Contract F30602-85-C-0255 through Rome Air Development Center while the author was a graduate student at the Georgia Institute of Technology.     

类对象测试数据自动生成研究

为了解决类对象测试数据的自动化生成问题,研究了基于UML(Unified Modeling Language,统一建模语言)状态图和遗传算法的类对象测试数据自动生成枝术.在扩展海明距离法的基础上进行适应度缩放,提出了一种在遗传算法中生成类对象测试数据的适应度函数改进方法,提高了遗传算法的收敛速度.最后将方法实验于实际系统,实验结果显示在生成类对象测试数据的效率上有明显的提高.     

基于演化算法的结构测试数据自动生成方法评述

结构测试数据自动生成是结构测试结构测试数据自动生成方法后,重点对基于演化算法的结构测试数据自动生成方法加以评述.归纳了该方法的基本思想和基本流程,按照适应度函数构造方式的不同将其划分为面向覆盖法、面向距离法和综合法三大类,并结合相关文献分析了这三类方法各自的技术特点,比较了各自的优劣.最后,指出了存在的不足,探讨了发展方向.     

Detecting buffer overflow via automatic test input data generation

Buffer overflows cause serious problems in various categories of software systems. In critical systems, such as health-care, nuclear or aerospace software applications, a buffer overflow may cause severe threats to humans or severe economic losses. If they occur in network or security applications, they can be exploited to gain administrator privileges, perform system attacks, access unauthorized data, or misuse the system. This paper proposes a combination of genetic algorithms, linear programming, evolutionary testing, and static and dynamic information to detect buffer overflows. The newly proposed test input generation process avoids the need for human intervention to define and tune genetic algorithm weights and therefore it becomes completely automated. The process that guides the genetic search towards the detection of buffer overflow relies on a fitness function that takes into account static and dynamic information. Reported results of our case studies, consisting of two sets of open-source programs show that the new process and fitness function outperform previously published approaches.     

基于混合遗传算法的测试数据自动生成研究

为了提高软件测试中测试数据自动生成的效率,提出了一种基于混合遗传算法的测试数据自动生成的方法.在传统的遗传算法中引入模拟退火的思想,先利用遗传算法快速搜索到近优解,再使用模拟退火算法局部寻优,实现两种算法的优势互补.实验结果表明,该算法有效避免了早熟问题,具有收敛速度快、搜索效率高等特点,能够更加快速地自动生成测试数据.     

基于改进PSO算法的测试数据自动生成研究

为了提高软件测试中测试数据自动生成的效率,提出了一种基于改进PSO算法的测试数据自动生成的方法。通过在标准的PSO算法中引入人工免疫的思想,保持了群体的多样性,从而有效避免标准PSO算法易陷入局部最优的问题,提高了算法全局搜索的能力,增强了算法的整体性能。实验结果表明,利用改进后的PSO算法寻找最优解所需的迭代次数和时间明显少于标准粒子群算法,生成测试数据的速度快、效率高。     

Using program data-state scarcity to guide automatic test data generation

Finding test data to cover structural test coverage criteria such as branch coverage is largely a manual and hence expensive activity. A potential low cost alternative is to generate the required test data automatically. Search-based test data generation is one approach that has attracted recent interest. This approach is based on the definition of an evaluation or cost function that is able to discriminate between candidate test cases with respect to achieving a given test goal. The cost function is implemented by appropriate instrumentation of the program under test. The candidate test is then executed on the instrumented program. This provides an evaluation of the candidate test in terms of the “distance” between the computation achieved by the candidate test and the computation required to achieve the test goal. Providing the cost function is able to discriminate reliably between candidate tests that are close or far from covering the test goal and the goal is feasible, a search process is able to converge to a solution, i.e., a test case that satisfies the coverage goal. For some programs, however, an informative cost function is difficult to define. The operations performed by these programs are such that the cost function returns a constant value for a very wide range of inputs. A typical example of this problem arises in the instrumentation of branch predicates that depend on the value of a Boolean-valued (flag) variable although the problem is not limited to programs that contain flag variables. Although methods are known for overcoming the problems of flag variables in particular cases, the more general problem of a near constant cost function has not been tackled. This paper presents a new heuristic for directing the search when the cost function at a test goal is not able to differentiate between candidate test inputs. The heuristic directs the search toward test cases that produce rare or scarce data states. Scarce inputs for the cost function are more likely to produce new cost values. The proposed method is evaluated empirically for a number of example programs for which existing methods are inadequate.