Analyzing clusters of class characteristics in OO applications

The transition from Java 1.4 to Java 1.5 has provided the programmer with more flexibility due to the inclusion of several new language constructs, such as parameterized types. This transition is expected to increase the number of class clusters exhibiting different combinations of class characteristics. In this paper we investigate how the number and distribution of clusters are expected to change during this transition. We present the results of an empirical study were we analyzed applications written in both Java 1.4 and 1.5. In addition, we show how the variability of the combinations of class characteristics may affect the testing of class members.     

Predicting weekly defect inflow in large software projects based on project planning and test status

Defects discovered during the testing phase in software projects need to be removed before the software is shipped to the customers. The removal of defects can constitute a significant amount of effort in a project and project managers are faced with a decision whether to continue development or shift some resources to cope with defect removal. The goal of this research is to improve the practice of project management by providing a method for predicting the number of defects reported into the defect database in the project. In this paper we present a method for predicting the number of defects reported into the defect database in a large software project on a weekly basis. The method is based on using project progress data, in particular the information about the test progress, to predict defect inflow in the next three coming weeks. The results show that the prediction accuracy of our models is up to 72% (mean magnitude of relative error for predictions of 1 week in advance is 28%) when used in ongoing large software projects. The method is intended to support project managers in more accurate adjusting resources in the project, since they are notified in advance about the potentially large effort needed to correct defects.     

虚拟维修模型的校验方法研究

介绍了虚拟雏修与建模仿真的关系,分析了虚拟维修模型校验的特点,研究了虚拟维修模型校验的方法,分别应用置信区间法、假设检验法、Bayes法、TIC法、频谱分析法从静态性能和动态性能两个方面对模型实施校验,从而为虚拟维修模型准确性、实时性提供了较为可靠的验证方法.为虚拟维修模型的校验提供一套系统的理论和方法,有利于虚拟维修模型得到更好应用.     

布尔表达式的化简与并行排序网络验证

为设计出能够验证并行排序网络正确性的有效工具,根据[0,1]原理和布尔函数的特点和性质,提出一种布尔表达式的化简算法,并根据此算法设计出验证工具.对[0,1]原理和布尔函数的特点和性质进行了讨论,指出有利于化简操作的性质.设计出的工具能够根据并行排序网络的参数显示网络图形、自动生成布尔表达式并实现化简验证,工具的输出有利于对排序网络的分析,也可以用于辅助排序网络的设计和优化.实验结果表明了该工具的有效性.     

体系结构验证评估与体系结构融合研究

针对体系结构与体系结构验证评估之间的相互关系,以及两者之间如何进行耦合的问题,提出了一种紧耦合的方案,将体系结构验证评估模块嵌入到体系结构中,从而实现了评估的动态性、实时性,使得评估的结果能够及时反应到体系结构中.     

面向事实性问题的答案选择技术研究综述

答案选择是问答系统的一个关键步骤,它的任务是从候选答案集中选择出最佳答案返给用户,其主要研究内容包括答案选择的标准、方法及评价。该文首先介绍了主要的答案选择标准,分析了答案选择标准与问答系统评测之间的关系。然后将答案选择策略分为基于冗余的策略、基于相似性的策略和基于推理的策略,分别对每种策略的主要答案选择方法和特点进行了概述。随后又介绍了答案选择的评价指标及答案验证评测。最后讨论了答案选择所面临的主要问题,并对其未来的发展方向进行了展望。     

基于Struts的Web开发

Struts是目前比较流行的一个基于MVC设计模式的JavaWeb应用框架,文章详细地阐述了Struts框架实现MVC的构架机制及其核心组件和框架特点,介绍了集成在Struts框架内部的Validator框架在具体Web系统中的开发应用,比较了基于该验证框架的两种验证的优缺点。     

Controller certification

A modern problem from aerospace control involves the certification of a large set of potential controllers with either a single plant or a fleet of potential plant systems, with both plants and controllers being MIMO and, for the moment, linear. Experiments on a limited number of controller/plant pairs should establish the stability and a certain level of margin of the complete set. We consider this certification problem for a set of controllers and provide algorithms for selecting an efficient subset for testing. This is done for a finite set of candidate controllers and, at least for SISO plants, for compact infinite set. In doing this, the ν-gap metric will be the main tool. Computational examples are given, including one of certification of an aircraft engine controller. The overarching aim is to introduce truly MIMO margin calculations and to understand their efficacy in certifying stability over a set of controllers and in replacing legacy single-loop gain and phase margin calculations.     

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.     

Computing the Kantorovich Distance for Images

Computing the Kantorovich distance for images is equivalent to solving a very large transportation problem. The cost-function of this transportation problem depends on which distance-function one uses to measure distances between pixels.In this paper we present an algorithm, with a computational complexity of roughly order (N2), where N is equal to the number of pixels in the two images, in case the underlying distance-function is the L1-metric, an approximation of the L2-metric or the square of the L2-metric; a standard algorithm would have a computational complexity of order (N3). The algorithm is based on the classical primal-dual algorithm.The algorithm also gives rise to a transportation plan from one image to the other and we also show how this transportation plan can be used for interpolation and possibly also for compression and discrimination.