基于定值-引用链的测试用例优先级排序算法

测试用例优先级排序作为一种高效实用的回归测试技术,通常以测试用例的覆盖度作为优先级排序的量化指标,忽略了测试用例的其他测试性能。针对该问题,提出一种基于DU链的测试用例优先级排序算法。该算法 综合考虑 测试用例的DU链覆盖度和回归测试的错误检测能力,对测试用例优先级进行量化。与已有算法相比,该算法基于数据流覆盖,充分利用了测试执行的历史信息和程序模块的耦合信息,在排序过程中动态计算测试用例的优先级量化值。实验结果表明,采用优先级排序算法的测试用例集能在测试过程中以较短的时间发现更多的错误,有效地提高了回归测试的检错效率。     

基于历史信息的自适应测试用例优先级技术

在软件迭代开发的过程中,测试用例优先级技术因能有效地提高回归测试的效率,降低时间开销和人力成本,受到研究者的广泛关注,许多优化方法相继被提出。但是目前的研究多倾向于以需求和覆盖率作为排序准则,并且是一种静态排序。为此,提出一种基于历史信息的测试用例优先级技术,并在测试用例的执行过程中动态自适应地调整测试用例的优先级,以尽可能早地发现缺陷,达到预期的检错目标。在课题组开发的项目中运用该方法,验证了该方法的有效性。     

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。     

一种基于强化学习的持续集成环境中测试用例排序技术

在软件交付越来越强调迅速、可靠的当下,持续集成成为一项备受关注的技术.开发人员不断将工作副本集成到代码主干完成软件演化,每次集成会通过自动构建测试来验证代码更新是否引入错误.但随着软件规模的增大,测试用例集包含的测试用例越来越多,测试用例的覆盖范围、检错效果等特征也随着集成周期的延长而变化,传统的测试用例排序技术难以适用.基于强化学习的测试排序技术可以根据测试反馈动态调整排序策略,但现有的相关技术不能综合考虑测试用例集中的信息进行排序,这限制了它们的性能.提出一种新的基于强化学习的持续集成环境中测试用例排序方法——指针排序方法：方法使用测试用例的历史信息等特征作为输入,在每个集成周期中,智能体利用指针注意力机制获得对所有备选测试用例的关注程度,由此得到排序结果,并从测试执行的反馈得到策略更新的方向,在“排序-运行测试-反馈”的过程中不断调整排序策略,最终达到良好的排序性能.在5个规模较大的数据集上验证了所提方法的效果,并探究了使用的历史信息长度对方法性能的影响,方法在仅含回归测试用例的数据集上的排序效果,以及方法的执行效率.最后,得到如下结论：（1）与现有方法相比,指针排序方法能够随着软件版本的演化调整排序策略,在持续集成环境下有效地提升测试序列的检错能力.（2）指针排序方法对输入的历史信息长度有较好的鲁棒性,少量的历史信息即可使其达到最优效果.（3）指针排序方法能够很好地处理回归测试用例和新增测试用例.（4）指针排序方法的时间开销不大,结合其更好、更稳定的排序性能,可以认为指针排序方法是一个非常有竞争力的方法.     

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。     

基于需求的测试用例优先级排序

为提高软件测试用例执行效率,研究了测试用例优先级排序技术,提出了基于需求的测试用例优先级排序及动态调整算法.基于需求的测试用例优先级排序将与软件需求相关的影响因素应用于测试用例的排序,并且在测试执行过程中可以通过调整算法对测试用例的优先级进行动态调整.实验结果表明,采用基于需求的测试用例优先级排序可以提高软件缺陷检测率...     

面向CPU+GPU异构计算的多目标测试用例优先排序

测试用例优先排序是一种基于整个测试用例集以寻找最优测试用例执行序列的软件回归测试技术.由于其能够尽早地发现错误,同时应用灵活度高、不会漏掉重要测试用例等,在实际软件测试过程中可以有效提高测试效率.多目标测试用例优化排序是寻找同时覆盖多个测试准则的用例执行序列,通常采用演化算法优化求解,但执行时间较长,严重影响了在实际软件测试中的应用.采用先进的GPU图形卡通用并行计算技术,提出了面向CPU+GPU异构计算下的多目标测试用例优先排序技术,在NSGA-II算法中,实现了基于序列编码的适应度函数计算和交叉操作的GPU并行计算,在近6万行有效代码的工业界开源程序上实现了30倍的计算效率提升.同时,实验验证了不同并行策略的计算加速比,提出了切实可行的CPU+GPU异构计算模式,并提供了相应的原形工具.     

水利业务应用系统中软件测试方法研究及应用

软件测试是一项重要的软件确认活动,通过运行测试用例,旨在发现软件中存在的错误,保证交付出高质量的软件。为保证水利业务应用系统的质量,需要在建设过程中对系统进行充分的测试。研究测试用例的生成方法,包括逻辑覆盖和基本路径测试等白盒测试技术,以及等价划分和边界值分析等黑盒测试技术。选择防汛信息查询模块,重点探讨使用白盒和黑盒测试技术对该模块进行测试用例的生成,再运行测试用例对系统进行测试。2种测试技术能够有效地检测出系统中存在的错误,使开发人员能够及时修正错误。     

融合聚类算法和缺陷预测的测试用例优先排序方法

持续集成环境下,软件快速更新加快了回归测试执行的频率,但缺陷快速反馈的需求对回归测试又提出了更高要求。测试用例优先排序技术研究测试用例的重要性,通常将缺陷探测能力强的测试用例优先执行,使其提早发现软件缺陷,其可解决持续集成环境下的快速反馈需求。缺陷预测技术可通过被测系统代码特征和历史缺陷来预估信息预测软件在新版本中发现缺陷的可能性,传统基于聚类的测试用例优先排序方法大多未考虑不同类簇数和特征子集对聚类结果的影响。文中将缺陷预测应用到聚类优先排序方法,构建测试用例和代码关联矩阵,对测试用例进行聚类分析,结合缺陷预测结果和最大最小距离策略指导簇间和簇内排序。通过实验验证发现,类簇数和聚类特征子集选择对排序效果有一定影响,当未能获取最佳类簇数和特征子集时,相比单一的聚类优先排序方法,所提方法可更有效地提高回归测试效率。     

一种基于测试状态的回归测试方法

软件测试在软件生命周期中是一个非常重要的过程,而回归测试则又在软件测试中占有极其重要的地住。本文提出的RTSPS方法综合考虑了测试状态即测试频次、测试成本、错误检测率要求等多种因素来选择合适的回归测试策略,并对测试用例进行优先排序,可以有效地提高测试效率。     

一个划算的回归测试用例排序的成本-收益模型

回归测试是一个昂贵的测试过程,它验证修改的软件,确认是否给已经测试的代码引进了新的错误.因为在软件开发的过程中,测试人员给测试包中增加了新的测试用例,使软件确认更加困难.为了使回归测试更经济划算,提出了很多技术和方法.测试用例选择,测试包缩减,测试用例排序可以帮助我们减少需要运行的测试用例数目,同时能够更快地满足测试目标.测试用例排序是在满足某个性能目标的基础上,保证在测试过程中首先执行更重要的功能.以前的研究提出了一个性能目标:缺陷发现率(APFD),以及很多改善缺陷发现率的技术.但是这些技术运用起来非常昂贵,而且可能不会减少回归测试的成本.在以往研究的基础上,提出了一个成本-收益模型,为比较各种排序算法提供了一定依据.并在此基础上,提供了一个测试用例排序算法.     

On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques

Regression testing is an important activity in the software life cycle, but it can also be very expensive. To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One potential goal of test case prioritization techniques is to increase a test suite's rate of fault detection (how quickly, in a run of its test cases, that test suite can detect faults). Previous work has shown that prioritization can improve a test suite's rate of fault detection, but the assessment of prioritization techniques has been limited primarily to hand-seeded faults, largely due to the belief that such faults are more realistic than automatically generated (mutation) faults. A recent empirical study, however, suggests that mutation faults can be representative of real faults and that the use of hand-seeded faults can be problematic for the validity of empirical results focusing on fault detection. We have therefore designed and performed two controlled experiments assessing the ability of prioritization techniques to improve the rate of fault detection of test case prioritization techniques, measured relative to mutation faults. Our results show that prioritization can be effective relative to the faults considered, and they expose ways in which that effectiveness can vary with characteristics of faults and test suites. More importantly, a comparison of our results with those collected using hand-seeded faults reveals several implications for researchers performing empirical studies of test case prioritization techniques in particular and testing techniques in general     

Prioritizing JUnit Test Cases: An Empirical Assessment and Cost-Benefits Analysis

Test case prioritization provides a way to run test cases with the highest priority earliest. Numerous empirical studies have shown that prioritization can improve a test suite's rate of fault detection, but the extent to which these results generalize is an open question because the studies have all focused on a single procedural language, C, and a few specific types of test suites. In particular, Java and the JUnit testing framework are being used extensively to build software systems in practice, and the effectiveness of prioritization techniques on Java systems tested under JUnit has not been investigated. We have therefore designed and performed a controlled experiment examining whether test case prioritization can be effective on Java programs tested under JUnit, and comparing the results to those achieved in earlier studies. Our analyses show that test case prioritization can significantly improve the rate of fault detection of JUnit test suites, but also reveal differences with respect to previous studies that can be related to the language and testing paradigm. To investigate the practical implications of these results, we present a set of cost-benefits models for test case prioritization, and show how the effectiveness differences observed can result in savings in practice, but vary substantially with the cost factors associated with particular testing processes.     

Factor oriented requirement coverage based system test case prioritization of new and regression test cases

Test case prioritization involves scheduling test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also that detects the most severe faults at the earliest in its testing life cycle. In this paper, we propose to put forth a model for system level test case prioritization (TCP) from software requirement specification to improve user satisfaction with quality software that can also be cost effective and to improve the rate of severe fault detection. The proposed model prioritizes the system test cases based on the six factors: customer priority, changes in requirement, implementation complexity, completeness, traceability and fault impact. The proposed prioritization technique is validated with two different validation techniques and is experimented in three phases with student projects and two sets of industrial projects and the results show convincingly that the proposed prioritization technique improves the rate of severe fault detection.     

基于蚁群算法的测试用例优先排序

测试用例优先排序技术通过优化测试用例的执行次序来提高软件测试的效率,是增强型软件测试和回归测试的重要研究课题。针对基于需求的测试用例优先排序问题,提出了一种基于蚁群算法的求解方法,采用不同的测试用例间距离及用例序列评价策略,给出了该方法的2种不同实现方式。首先,针对黑盒测试特点,设计了基于需求的一般性测试用例序列评价指标;其次,提出测试用例吸引度概念,基于测试用例吸引度定义了测试用例间的距离;然后,给出了信息素更新策略、最优解集更新策略、局部最优解突变策略等主要设计策略,分别实现了该方法基于距离和基于指标的2种实现方式。实验结果表明,该方法具有很好的全局寻优能力,整体效果上优于粒子群算法、遗传算法和随机测试。     

A history-based cost-cognizant test case prioritization technique in regression testing

Software testing is typically used to verify whether the developed software product meets its requirements. From the result of software testing, developers can make an assessment about the quality or the acceptability of developed software. It is noted that during testing, the test case is a pair of input and expected output, and a number of test cases will be executed either sequentially or randomly. The techniques of test case prioritization usually schedule test cases for regression testing in an order that attempts to increase the effectiveness. However, the cost of test cases and the severity of faults are usually varied. In this paper, we propose a method of cost-cognizant test case prioritization based on the use of historical records. We gather the historical records from the latest regression testing and then propose a genetic algorithm to determine the most effective order. Some controlled experiments are performed to evaluate the effectiveness of our proposed method. Evaluation results indicate that our proposed method has improved the fault detection effectiveness. It can also been found that prioritizing test cases based on their historical information can provide high test effectiveness during testing.     

测试用例优先级排序技术研究

测试用例的优化技术是软件测试的重要组成部分,其目的是将测试用例按照设定的规则进行排序,以便对测试效率有所提高。介绍了其背景和基本概念,从初始测试用例的优先级排序及回归测试的测试用例优先级排序进行概述,比较了各种优先级技术所适用的测试环境和优点,指出了测试用例优化技术的一些缺点和未来的发展方向。     

基于动态相似度的错误定位优先排序方法

在软件测试中,错误定位优先排序通过优化测试用例的执行次序来提高错误定位的效果,并将检测错误和定位错误相结合,以降低测试成本。 提出了一种基于动态相似度的错误定位优先排序方法,在相似度计算中,引入了语句怀疑度,提高了相似度计算的有效性以及错误定位的准确度;同时分析并验证了不同测试用例优先排序算法对后续定位错误的影响。在6个C基准程序上,针对3种广泛采用的测试用例优先排序算法和2种错误定位技术进行了实验,结果表明提出的方法能提高错误定位的准确度和效率。