软件评审

软件评审是软件质量控制的一个重要手段．本文简要介绍了软件评审目标，过程和准则等内容，希望能从除度量与测试以外的角度来对电子政务系统建设中软件质量进行控制。     

基于面向对象系统的质量度量模型PMMSQ的设计和实现

本文首先从软件质量度量的角度出发,阐述了软件质量度量的理论与方法,介绍了ISO9126模型;针对已有软件质量度量模型中存在的问题,提出了一个适合于软件质量度量实践的实用软件质量度量模型（PMMSQ）,并详细介绍了面向对象系统软件质量度量的步骤;最后,根据PMMSQ模型完成了软件度量活动的核心工作--对象特征集存储库的设
计和实现。     

从集合表达式产生测试数据的方法

软件测试保证和提高了软件质量,因此成为软件界最为关心的问题之一.测试数据的好坏直接影响软件测试的效果.形式规格说明中的前置条件可用来产生测试数据.而前置条件是基于关系操作符的谓词表达式.给出了一个针对集合关系表达式的测试准则和产生符合测试准则的测试数据的算法,根据该测试准则产生的测试数据对于集合条件表达式有比较好的错误检测能力.从给出的数据库查询的例子说明了该方法对于检测代码错误的有效性.     

ERP软件质量模糊综合评价方法

为给ERP软件供应商和应用ERP软件的企业提供一种定量的ERP软件质量评价方法,基于Waiters和McCall3层软件质量度量模型,建立了适用于ERP软件的质量评价模型。研究了运用模糊理论对ERP软件质量进行评价的方法。通过模糊运算获得了ERP软件质量的评价结果。通过一个典型ERP软件实例,验证了该方法的有效性和可行性。     

面向对象软件度量的研究

软件度量是提高和保证软件质量的关键。在给出的面向对象软件度量的结构模型基础上,讨论了实现该模型要解决的关键问题。通过度量结果的分析和评价,能够有效地对面向对象软件的测试和提高软件质量提供科学的依据。     

混源软件质量模型与度量方法研究

由于混源软件包含自主代码、开源代码等不同来源代码,从而具有更高的多样性和复杂性,对其质量的度量评估与传统软件存在极大区别。为了度量混源软件质量,建立混源软件质量度量模型和方法是非常必要的。通过分析混源软件质量特性,提出混源软件质量模型。然后利用层次分析法、幂性法及线性法构建度量方法体系。最后对UbuntuKylin操作系统进行了实验性的度量评估,验证了模型与方法的可行性和有效性。     

软件使用统计测试方法的研究

软件开发实践表明,传统软件统计测试方法在提高软件可靠性和度量软件质量方面显现很多不足,阐述了一种改进的基于Markov链模型的软件统计测试方法的技术细节和实施步骤,用此方法对某控制软件的测试进行了具体的研究,对实例软件运用此方法进行测试提出了一个框架方案.实例验证表明,软件使用统计测试方法能够更加经济有效地保障软件质量.     

基于模糊推理的软件测试度量方法

如何形成软件测试有效的成本效益策略一直以来都是软件工程的重要研究内容.许多研究者致力于软件测试的有效性和质量度量的研究并已取得大量成果.但是,软件测试中最重要的问题之一--测试度量中内在的不确定和不相关的关系仍然没有解决.由此,提出一种新的基于模糊逻辑的软件测试质量和测试有效性度量方法,探讨了基于软件质量特性和相似性推理的方法,试图解决不同测试工作任务之间的测试质量和有效性一致的问题.同时提供实验的结果证明提出的方法的可行性.     

面向对象设计中软件度量学的进展

一、引言软件度量学作为软件工程的一个研究方向,其目标是用定量方去管理和控制软件开发过程,评价软件质量,合理地分配资源。二十多年来在软件度量的理论荃础,软件度量方法、验证、应用模式等方面已有不少结果。     

统计测试的软件可靠性保障能力研究

研究了统计测试对不同软件的测试能力,提出将测试有效性信息综合到软件可靠性评估模型中,以提高可靠性估计的针对性和精度.通过实验证明了该方法的合理性.该方法为高可靠性的软件质量保障要求提供了途径和可能.     

An Experimental Evaluation of Data Flow and Mutation Testing

Two experimental comparisons of data flow and mutation testing are presented. These techniques are widely considered to be effective for unit-level software testing, but can only be analytically compared to a limited extent. We compare the techniques by evaluating the effectiveness of test data developed for each. We develop ten independent sets of test data for a number of programs: five to satisfy the mutation criterion and five to satisfy the all-uses data-flow criterion. These test sets are developed using automated tools, in a manner consistent with the way a test engineer might be expected to generate test data in practice. We use these test sets in two separate experiments. First we measure the effectiveness of the test data that was developed for one technique in terms of the other. Second, we investigate the ability of the test sets to find faults. We place a number of faults into each of our subject programs, and measure the number of faults that are detected by the test sets. Our results indicate that while both techniques are effective, mutation-adequate test sets are closer to satisfying the data flow criterion, and detect more faults.     

Automatically performing weak mutation with the aid of symbolic execution,concolic testing and search-based testing

Automating software testing activities can increase the quality and drastically decrease the cost of software development. Toward this direction, various automated test data generation tools have been developed. The majority of existing tools aim at structural testing, while a quite limited number aim at a higher level of testing thoroughness such as mutation. In this paper, an attempt toward automating the generation of mutation-based test cases by utilizing existing automated tools is proposed. This is achieved by reducing the killing mutants’ problem into a covering branches one. To this extent, this paper is motivated by the use of state of the art techniques and tools suitable for covering program branches when performing mutation. Tools and techniques such as symbolic execution, concolic execution, and evolutionary testing can be easily adopted toward automating the test input generation activity for the weak mutation testing criterion by simply utilizing a special form of the mutant schemata technique. The propositions made in this paper integrate three automated tools in order to illustrate and examine the method’s feasibility and effectiveness. The obtained results, based on a set of Java program units, indicate the applicability and effectiveness of the suggested technique. The results advocate that the proposed approach is able to guide existing automating tools in producing test cases according to the weak mutation testing criterion. Additionally, experimental results with the proposed mutation testing regime show that weak mutation is able to speedup the mutant execution time by at least 4.79 times when compared with strong mutation.     

Automatic test data generation using genetic algorithm and program dependence graphs

The complexity of software systems has been increasing dramatically in the past decade, and software testing as a labor-intensive component is becoming more and more expensive. Testing costs often account for up to 50% of the total expense of software development; hence any techniques leading to the automatic generation of test data will have great potential to considerably reduce costs. Existing approaches of automatic test data generation have achieved some success by using evolutionary computation algorithms, but they are unable to deal with Boolean variables or enumerated types and they need to be improved in many other aspects. This paper presents a new approach utilizing program dependence analysis techniques and genetic algorithms (GAs) to generate test data. A set of experiments using the new approach is reported to show its effectiveness and efficiency based upon established criterion.     

On the adoption of MC/DC and control-flow adequacy for a tight integration of program testing and statistical fault localization

ContextTesting and debugging consume a significant portion of software development effort. Both processes are usually conducted independently despite their close relationship with each other. Test adequacy is vital for developers to assure that sufficient testing effort has been made, while finding all the faults in a program as soon as possible is equally important. A tight integration between testing and debugging activities is essential.ObjectiveThe paper aims at finding whether three factors, namely, the adequacy criterion to gauge a test suite, the size of a prioritized test suite, and the percentage of such a test suite used in fault localization, have significant impacts on integrating test case prioritization techniques with statistical fault localization techniques.MethodWe conduct a controlled experiment to investigate the effectiveness of applying adequate test suites to locate faults in a benchmark suite of seven Siemens programs and four real-life UNIX utility programs using three adequacy criteria, 16 test case prioritization techniques, and four statistical fault localization techniques. We measure the proportion of code needed to be examined in order to locate a fault as the effectiveness of statistical fault localization techniques. We also investigate the integration of test case prioritization and statistical fault localization with postmortem analysis.ResultThe main result shows that on average, it is more effective for a statistical fault localization technique to utilize the execution results of a MC/DC-adequate test suite than those of a branch-adequate test suite, and is in turn more effective to utilize the execution results of a branch-adequate test suite than those of a statement-adequate test suite. On the other hand, we find that none of the fault localization techniques studied can be sufficiently effective in suggesting fault-relevant statements that can fit easily into one debug window of a typical IDE.ConclusionWe find that the adequacy criterion and the percentage of a prioritized test suite utilized are major factors affecting the effectiveness of statistical fault localization techniques. In our experiment, the adoption of a stronger adequacy criterion can lead to more effective integration of testing and debugging.     

基于多属性决策和交叉熵优化多维测试覆盖

随着软件规模和复杂性越来越高,人们对软件质量保证意识也就逐步增强。如何展开测试、优化测试、选择测试方案及提高测试效率成为目前软件工程中面临的现实问题。利用测试覆盖的多维度量指标,提出综合测试覆盖率、测试覆盖率满意度及测试效率作为优化测试方案选择的效用属性,将综合测试覆盖方案应用于马尔可夫测试模型中进行优化,获得了较优的覆盖测试剖面,优化了测试流程。实验结果表明优化的测试覆盖剖面比随机测试能够减少测试用例数,提高缺陷检测效率。     

基于演化数据的软件缺陷预测性能改进

软件持续演化已经是不争的事实,演化意味着需求的变化,也就必然导致了缺陷的不断产生.现有的缺陷预测技术多偏重于基于软件工作制品,如文档、代码、测试用例等的属性来预测缺陷,但如果把软件看作一种物种,其生命周期内的演化本质上是一个物种的逐步进化,其缺陷的表现也必然带着该物种的特征,而且还受到进化历史中的演化轨迹的影响.已有一些研究人员开始研究软件演化过程,并提出了一些演化度量元.研究和提出了可以刻画软件演化轨迹的两类演化度量元,并通过案例研究,建立缺陷预测模型.在6个著名开源软件数据集上训练和验证了由软件演化度量元建立的缺陷预测模型,获得了良好的预测性能,验证了演化度量元对缺陷预测性能的改进.     

A formal analysis of MCDC and RCDC test criteria

The Modified Condition Decision Coverage (MCDC) test criterion is a mandatory requirement for the testing of avionics software as per the DO‐178B standard. This paper presents a formal analysis for the three different forms of MCDC. In addition, a recently proposed test criterion, Reinforced Condition Decision Coverage (RCDC), has also been investigated in comparison with MCDC. In contrast with the earlier analysis approaches that have been based on empirical and probabilistic models, the principles of Boolean ogic are used here to study the fault detection effectiveness of the MCDC and RCDC criteria. Based on the properties of Boolean specifications, the analysis identifies the detection conditions for six kinds of faults. The results allow the measurement of the effort required in testing and the effectiveness of generated test sets satisfying the MCDC and RCDC criteria. Copyright © 2004 John Wiley & Sons, Ltd.     

Quality assessment in competition-based software crowdsourcing

Quality assessment is a critical component in crowdsourcing-based software engineering (CBSE) as software products are developed by the crowd with unknown or varied skills and motivations. In this paper, we propose a novel metric called the project score to measure the performance of projects and the quality of products for competitionbased software crowdsourcing development (CBSCD) activities. To the best of our knowledge, this is the first work to deal with the quality issue of CBSE in the perspective of projects instead of contests. In particular, we develop a hierarchical quality evaluation framework for CBSCD projects and come up with two metric aggregation models for project scores. The first model is a modified squale model that can locate the software modules of poor quality, and the second one is a clustering-based aggregationmodel, which takes different impacts of phases into account. To test the effectiveness of the proposed metrics, we conduct an empirical study on TopCoder, which is a famous CBSCD platform. Results show that the proposed project score is a strong indicator of the performance and product quality of CBSCD projects.We also find that the clustering-based aggregation model outperforms the Squale one by increasing the percentage of the performance evaluation criterion of aggregation models by an additional 29%. Our approach to quality assessment for CBSCD projects could potentially facilitate software managers to assess the overall quality of a crowdsourced project consisting of programming contests.     

由需求得到回归测试用例排序技术

为了提高回归测试用例集的测试效率和有效性,提出由需求得到回归测试用例排序技术及其实现算法。由需求得到回归测试用例排序技术,将与软件需求相关的需求描述度、需求实现复杂度、需求稳定度和需求覆盖度等因素应用于测试用例排序,以缺陷检测加权平均百分比作为度量标准。通过实验,比较排序后用例和未排序用例缺陷检测情况,实验结果表明该技术排序后的回归测试用例集,能够尽早地发现更多的软件错误,有效提高回归测试效率,保证软件质量。     

A fuzzy clustering methodology for linguistic opinions in group decision making

The objective of this paper is to automate a decision-aid tool, which provides homogeneous clusters from a set of heterogeneous opinions for a particular criterion to evaluate an item under group decision scheme. In such situation, decision-making by a group of experts becomes more realistic and consistent while they provide more or less homogeneous responses. But in real practice, the homogeneity among the opinions for a specific criterion to evaluate an item is not maintained due to the diversity among the experts’ several cognitive factors as well as biasness. As a result, the group's overall effectiveness is suffered and making a true decision becomes difficult as well as sometimes confusing. In order to avoid the heterogeneity among the opinions (fuzzy numbers), we propose here a fuzzy clustering methodology based on a fuzzy distance measure. Also a ranking index is introduced on the basis of Ordered Weighted Average (OWA) operator. Finally, a fuzzy multi-criteria decision-making problem on a flight simulator software development project is considered here to employ the proposed technique. The results are discussed and compared.