基于函数调用路径的测试用例优先级排序

基于覆盖的优先级排序技术通常以代码覆盖信息作为测试用例的特征加以度量,忽略了其他优先级的影响因素,缺乏全面性和动态性。针对该问题,提出基于函数调用路径的测试用例优先级排序方法。以函数调用路径为基础,通过对源代码新旧版本的对比,分析回归测试影响域,确定回归测试用例集的范围。将测试用例函数调用路径覆盖能力、单元测试时函数中检测出缺陷的个数,以及函数的扇入系数等影响因素应用于优先级排序,确定测试用例优先级量化方法,并在测试执行过程中,通过调整算法实现优先级的动态调整,优化优先级排序。实验结果表明,优先级量化方法能提高测试的缺陷检测率,发现程序中的缺陷,降低测试成本。     

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。     

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。     

基于测试用例设计信息的回归测试优先级算法

优先级技术是一种高效实用的回归测试技术.文中针对现有优先级技术未能有效使用测试用例设计信息的不足,提出了一组新的回归测试优先级动态调整算法.与已有方法相比,新算法充分考虑了测试用例的设计信息,能够通过及时捕捉和利用测试执行信息对测试用例优先级进行动态调整,具有时间复杂度低、检错效率高等优点.将其应用于Windows平台下应用软件的回归测试结果表明,新算法有益于在短时间内检测出更多的错误.     

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。     

基于改进遗传算法的回归测试用例优先级排序

由于优先级排序测试效果较差、执行效率低,为此提出基于改进遗传算法的回归测试用例优先级排序方法.通过自然选择与遗传理论作为基本条件,结合群体中染色体任意信息交换机制和生物进化过程内适者生存规则,完成遗传算法,接着引入禁忌搜索方法,增强排序的局部超强搜索能力,然后在回归测试的过程内,利用多个目标函数对回归测试用例优先级排序以及评价,完成回归测试用例优先级排序.实验结果表明,经过时间以及覆盖的角度考察,执行时间较短,平均分支较大,说明该序列优良,本文方法的回归测试效果较好,执行效率高.     

基于圈复杂度的静态测试用例排序方法

针对如何排列测试用例的问题,提出一种基于圈复杂度的静态测试用例排序方法。首先介绍圈复杂度和基于方法覆盖的测试用例排序算法;然后将圈复杂度应用到排序算法中,设计了两种新的排序算法;最后通过实验,检测改进方法在错误检测方面的有效性。实验结果表明,与已有的几种优先级技术对比,上述改进方法能够达到更高的错误检测效率,有利于提高测试效率。     

基于OTT策略的组合测试用例优先级排序方法

在组合测试用例优先级排序问题中,通常采用组合覆盖率为排序标准,该方法能够尽快满足覆盖率的要求,但其排序影响因子较为单一,缺陷检测能力不稳定。针对此问题,该文结合One-Test-at-a-Time（OTT）策略提出了一种在线调整的组合测试用例优先级排序方法,引入多重待覆盖率、测试用例失效率和测试用例重要程度3个影响因子用于衡量组合测试用例优先级。根据测试用例的在线测试反馈信息,该方法能够实时调整组合测试用例的优先级,增加了优先级排序的准确性。实验结果表明,相较于original、基于单一覆盖和多重覆盖的组合测试优先级排序方法,该方法在缺陷检测能力和稳定性上具有更强的竞争力。     

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...     

基于函数调用路径的回归测试用例选择排序方法研究

针对在回归测试过程中,因为不断修复软件中存在的缺陷所造成的测试工作量大、测试效率低等问题,论文将测试用例选择与优先级排序技术相结合,以面向函数调用的路径覆盖生成方法为基础,提出了一种面向函数调用路径（Functions Calling Path, FCP）的测试用例选择与排序方法。首先根据函数调用关系图,对程序中被修改函数与其他函数的关联性进行分析,从初始测试用例集中选择测试用例,形成回归测试用例集;然后对这些测试用例进行优先级排序,并动态地调整优先级排序结果;最后,对优先级排序结果进行再次选择,确定最小的回归测试用例集。实验结果表明,测试用例选择与排序方法对优化回归测试用例是有效的,大大减少了回归测试用例数量,降低了回归测试成本。     

基于离散粒子群算法的测试用例优先排序

测试用例优先排序技术能够有效提高回归测试效率,是软件测试的热点研究课题之一。针对基于需求的测试用例优先排序方法可操作性差的问题,提出了一种改进的基于测试点覆盖和离散粒子群优化算法的求解方法（TCP-DPSO）。首先,把影响排序的各种因素分为测试收益型因素和测试成本型因素两大类,通过加权平均的方式进行归一化,得到基于需求的通用测试平均收益率评价指标;然后,利用交换子和基本交换序列定义粒子的位置和速度,借鉴遗传算法（GA）变异策略引入变异算子,采用时变惯性权重调整粒子的探索能力和开发能力,促进可持续进化和逼近优化目标。实验结果表明,TCP-DPSO在最优解质量上与遗传算法相当,大幅优于随机测试,在最优解成功率和平均求解时间上优于遗传算法,具有更好的算法稳定性。     

测试用例排序的研究

回归测试是一个成本很高的测试过程。为了减少回归测试的成本,可以使用测试用例排序技术。测试用例排序是指按照事先确定的目标重新安排测试用例集中测试用例的执行次序,使得具有高优先级的测试用例比低优先级的测试用例在测试过程中更早执行。本文描述了测试用例排序问题;给出了两个一般测试用例排序算法,即总计排序算法和 附加排序算法;根据不同的覆盖准则（如语句、分支和定义-使用等）,可以从这两个一般算法得到对应的排序算法;最后,讨论了测试用例排序算法的有效性。     

Automatic generation of basis test paths using variable length genetic algorithm

Path testing is the strongest coverage criterion in white box testing. Finding target paths is a key challenge in path testing. Genetic algorithms have been successfully used in many software testing activities such as generating test data, selecting test cases and test cases prioritization. In this paper, we introduce a new genetic algorithm for generating test paths. In this algorithm the length of the chromosome varies from iteration to another according to the change in the length of the path. Based on the proposed algorithm, we present a new technique for automatically generating a set of basis test paths which can be used as testing paths in any path testing method. The proposed technique uses a method to verify the independency of the generated paths to be included in the basis set of paths. In addition, this technique employs a method for checking the feasibility of the generated paths. We introduce new definitions for the key concepts of genetic algorithm such as chromosome representation, crossover, mutation, and fitness function to be compatible with path generation. In addition, we present a case study to show the efficiency of our technique. We conducted a set of experiments to evaluate the effectiveness of the proposed path generation technique. The results showed that the proposed technique causes substantial reduction in path generation effort, and that the proposed GA algorithm is effective in test path generation.     

面向回归测试的代码变更影响度量模型

软件待测版本相对上一个版本的代码变更,会对已有特性带来潜在的质量风险,这一风险水平直接与回归测试用例的优先级相关联.回归测试设计过程中的一个重要问题是如何衡量代码变更对回归测试用例优先级的影响.本文在回归测试用例优先级评估模型的基础上,从测试覆盖的角度建立起回归测试用例与代码变更的直接关联,从代码整体耦合性的角度建立起回归测试用例与代码变更的间接关联,分析了代码变更对回归测试的显性影响和隐性影响,进而结合回归测试用例优先级的评估要求提出了一个新的度量模型.实验结果显示,使用该模型度量代码变更对回归测试用例优先级的影响水平,可以得到比较全面和客观的定量结果,从而为回归测试用例优先级的评估提供有效的支持.     

利用佳点集遗传算法的白盒测试用例优先排序

在软件演化过程中,测试用例优先排序作为一种高效实用的回归测试技术,对于提高缺陷的早期检测速率和降低测试成本有重要意义。针对传统遗传算法在白盒测试用例优先排序中收敛速度慢和稳定性差的问题,采用佳点集遗传算法求解白盒测试用例优先排序问题。算法根据程序实体覆盖矩阵对个体进行编码,以程序实体覆盖平均百分比作为适应度函数,采用随机抽样选择算子和佳点集交叉算子产生新一代种群。实验选择6个典型的基准开源项目,以语句、分支和方法作为程序实体,实验结果表明佳点集遗传算法收敛速度快、稳定性好,为回归测试提供了一个有效的测试用例优先排序方法,有助于尽早发现软件缺陷,降低测试成本。     

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Test case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the fault exposing potential (FEP) parameters associated to the test cases. The FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a cost‐effective FEP approximation for prioritizing modified condition decision coverage (MCDC) test cases. A strict negative correlation between the FEP of an MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing.     

An improved method for test case prioritization by incorporating historical test case data

Test case prioritization reorders test cases from a previous version of a software system for the current release to optimize regression testing. We have previously introduced a technique for test case prioritization using historical test case performance data. The technique was based on a test case prioritization equation, which directly computes the priority of each test case using the historical information of the test case using an equation with constant coefficients. This technique was compared just with random ordering approach. In this paper, we present an enhancement of the aforementioned technique in two ways. First, we propose a new prioritization equation with variable coefficients gained according to the available historical performance data, which acts as a feedback from the previous test sessions. Second, a family of comprehensive empirical studies has been conducted to evaluate the performance of the technique. We have compared the proposed technique with our previous technique and the technique proposed by Kim and Porter. The experimental results demonstrate the effectiveness of the proposed technique in accelerating the rate of fault detection in history-based test case prioritization.     

Experiments with test case prioritization using relevant slices

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible and to gain confidence that changes to software do not introduce defects. Once developed, test suites are reused and updated frequently, and their sizes grow as software evolves. Due to time and resource constraints, an important goal during regression testing of software is to prioritize the execution of test cases in a suite so as to improve the chances of increasing the rate of fault detection. Prior techniques for test case prioritization are based on the total number of coverage requirements exercised by the test cases. In this paper, we present a new approach to prioritize test cases that takes into account the coverage requirements present in the relevant slices of the outputs of test cases. We have implemented three different heuristics based on our relevant slicing based approach to prioritize test cases and conducted experiments to compare the effectiveness of our techniques with those of the traditional techniques that only account for the total requirement coverage. Our detailed experimental study and results provide interesting insights into the effectiveness of using relevant slices for test case prioritization in terms of ability to achieve high rate of fault detection.