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.     

Some problems of computer-aided testing and “interview-like tests”

Computer-based testing – is an effective teacher’s tool, intended to optimize course goals and assessment techniques in a comparatively short time. However, this is accomplished only if we deal with high-quality tests. It is strange, but despite the 100-year history of Testing Theory (see, Anastasi, A., Urbina, S. (1997). Psychological testing. Upper Saddle River, NJ: Prentice-Hall) there still exist some misconceptions. Modern wide-spread systems for computer based course management and testing reveal a set of problems corresponding to certain features of testing methods.     

Test frame updating in CPM testing of Prolog programs

Category Partition Method (CPM) is a general approach to specification-based program testing, where test frame reduction and refinement are two important issues. Test frame reduction is necessary since too many test frames may be produced, and test frame refinement is important since during CPM testing new information about test frame generation may be achieved and considered incrementally. Besides the information provided by testers or users, implementation related knowledge offers alternative information for reducing and refining CPM test frames. This paper explores the idea by proposing a call patterns semantics based test frame updating method for Prolog programs, in which a call patterns analysis is used to collect information about the way in which procedures are used in a program. The updated test frames will be represented as constraints. The effect of our test frame updating is two-fold. On one hand, it removes “uncared” data from the original set of test frames; on the other hand, it refines the test frames to which we should pay more attention. The first effect makes the input domain on which a procedure must be tested a subset of the procedure’s input domain, and the latter makes testers stand more chance to find out the faults that are more likely to show their presence in the use of the program under consideration. Our test frame updating method preserves the effectiveness of CPM testing with respect to the detection of faults we care. The test case generation from the updated set of test frames is also discussed. In order to show the applicability of our method an approximation call patterns semantics is proposed, and the test frame updating on the semantics is illustrated by an example.

Food security problems in sub-Saharan Africa: Operations Research as a tool of analysis

For many years, the author has been involved in teaching and research in the use of Operations Research as a tool of analysis to study food security problems in sub‐Saharan Africa, in particular, grass root problems of poor farmers. The paper presents an introduction to the way Operations Research methods have been applied in case studies and research projects, and discusses in retrospect the author's views on the strengths and limitations of the application of Operations Research. The paper has in particular been written for people who are not familiar with applications of Operations Research in agriculture, and are interested to learn about its potential usefulness in practice. The retrospective part is largely based on food security studies in e.g. Tanzania, Burkina Faso, Bénin, Togo and Eritrea and on participation in several interdisciplinary research programmes in Africa.     

模糊综合评判理论在研究性学习系统中的研究及应用

介绍了工程数学中的模糊综合评判理论以及相应树型向量推理算法,并将模糊综合推理机制运用到研究性学习系统中进行研究型应用。该系统中运用模糊综合评判对学习效果进行合理评判,得出综合成绩,以其作为改进研究性学习模式的参考评证,从而使研究性学习过程良性发展。     

浅谈高等学校财务与科研信息系统平台整合建设

整合高等学校财务核算系统与科研信息系统,建立统一的信息平台,可以解决高校科研项目管理、经费核算与指标控制相脱离问题。将科技人员信息系统、科技项目信息系统、科技成果信息系统及财务信息系统等信息孤岛相互整合,形成科研项目申报、立项、管理及财务核算、监督、分析、控制的一体化平台,提高科研项目管理与财务核算信息化水平。     

基于Web的岩心图文信息系统设计与实现

在石油地质部门,重要的地质参数都来自于钻井开采出来的岩心,岩心的管理就显得尤其重要。随着网络的普及,岩心图片以及相关信息能够及时地在远程查看分析,已经成为一个重要的课题。该文通过对岩心相关信息的分析和研究,提出了将最新的网络开发技术应用到岩心图文信息系统的设计与开发中的方法。按照该思路对系统进行设计,最后采用ASP.NET+SQL SERVER2005的方式对该系统进行开发,并以信息检索和三维重建原理为例介绍系统实现的主要方法。     

基于聚类方法的软件设计与应用

通过对聚类算法和技术的研究,设计和研制了层系优化配套软件,并应用到油田的开发层系细分与优化.通过分析东辛地区沙河街组的沙二段8-11砂层组8个小层的实体属性,用研制的细分开发层系软件对辛16断块的层系进行划分与组合评价,最终优选出合理的开发层系组合,表明了聚类方法技术在油藏高含水期细分开发层系中的可行性和科学性.     

新升格本科院校开放实验教学的研究与探讨

分析了新升格的地方应用型本科院校开放创新实验教学运行现状及存在的问题;从提高对实验室开放内涵的认识、构建知识与能力并重的开放实验教学内容体系、实验室开放模式、构建开放式实验教学保障体系等方面进行了深入探讨。结合开放实验教学的实际情况,对适合高校自身特点的开放实验教学模式作积极探索,为培养有创新精神的高技能、高素质人才开辟有效途径。     

C4KISR测试与评估应用框架的研究与实现

基于HLA/RTI和分布式数据库初步构建了一个开放的测试和评估应用框架,为战术C4KISR系统中的指挥、控制、通信(C3)以及典型飞行器武器系统的作战效能(K)提供一种有效的可扩展的测试评估环境.系统采用了基于HLA/RTI的分布式体系结构,与被测系统相连.建立了典型的层次化战术CAKISR系统效能指标体系,并实现相应的评估算法模型.基于Oracle数据库建立了通用的战术C4KISR系统试验数据库/用例库/算法库和相应的管理工具,并开发了通用的分析评估工具,支持评估指标的在线/离线计算和显示.