基于随机向量镜像策略改进ART算法

针对现有的镜像自适应随机测试(MART)、动态镜像自适应随机测试(DMART)等算法通过镜像函数生成的测试用例的随机性不足,使其有效性在不同程度上有明显下降的问题,提出基于随机向量镜像策略改进ART算法.首先将随机向量引入传统镜像函数,增大镜像测试用例间的差异性;然后将随机向量镜像函数运用到镜像策略中,改进现有的ART算法.实验结果表明,利用随机向量镜像策略可明显地提高镜像算法的有效性,并且该算法比传统ART算法的效率有显著提升.     

基于失效聚集度改进自适应随机测试算法

对于现有的自适应随机测试(ART)算法针对点状失效模式普遍存在有效性和效率均比随机测试(RT)差的问题,提出一种基于失效聚集度的自适应随机测试(CLART)算法,对传统的ART——固定候选集(FSCS)、区域排除随机测试(RRT)等算法进行改进。首先,根据被测程序的输入域估计主失效聚集度,确定局部搜索区域;然后,在区域内使用传统ART算法生成若干测试用例(TC)进行测试;若未发现错误,重新选择局部区域生成TC;重复这一过程直至发现错误。仿真实验显示在点状失效模式和块状失效模式下CLART算法的有效性比FSCS算法提高约20%,效率比FSCS算法提高约60%。实验结果表明CLART算法利用多个局部区域依次搜索可以快速锁定引发失效输入分布密集高的失效区域,从而提高测试的有效性和效率。     

关于蜕变测试和特殊用例测试的实例研究

提出了基于蜕变测试方法的集成测试环境MTest,进而为检验蜕变测试方法的能力和效率,以稀疏矩阵乘法程序为例设计了一组实验.该实验基于变异分析技术,分别以mutation score和错误发现率为度量指标,定量地分析和对比了特殊用例测试,以特殊测试用例和随机测试用例为源测试用例的蜕变测试这3种方法的测试能力和效率.该实验可在MTest测试环境下自动完成.实验结果表明,蜕变测试与特殊用例测试之间是互补的,而且就蜕变测试的源测试用例而言,随机测试用例在测试能力和效率上优于特殊测试用例.     

一种针对高维输入域的适应性随机测试改进性算法

适应性随机测试ART能够保证测试用例在输入域中更加均匀地分布,从而在失效检测能力上明显强于基本的随机测试,其中,固定候选集规模的ART算法 FSCS ART因具备较好的揭错能力而被广泛采用。然而随着输入域维度的升高,FSCS ART的失效检测效果显著降低。针对该问题,在从候选集中选择正式用例时综合考虑两种距离：候选点与已测用例之间的距离和它与中心点之间的距离,这样,输入域边缘的候选点的优先级得以降低,有效地克服了FSCS ART趋向于边缘的弊端。实验结果表明,改进后的算法针对高维输入域表现出更强的失效检测能力。     

复杂软件第三方自适应测试研究

复杂软件的第三方测试面临诸多问题,比如软件失效的过程复杂并具有不确定性、测试人员难以选择有代表性的测试用例、测试的时间和资源非常有限、测试效率比较低。结合软件工程实际,根据软件失效的关联特征,将测试过程抽象为节点序列的执行过程,从两个层次提出了一种面向测试用例相似性度量的复杂软件第三方自适应测试方法,并在实际测试项目中进行了应用。     

成对测试中的测试生成策略研究

测试用例生成是软件测试的关键，成对测试是根据特定的测试原则研究测试用例的产生方法。基于这一原则分别构造了IPO-H算法和IPO-V算法的测试以产生整个测试用例，具有产生的测试用例少，时间消耗小等优点。并与另一个成对测试的测试生成工具AETG(高效自动测试生成器)进行了比较、分析，证明了改进的IPO策略便于构造自动测试工具。     

基于自适应粒子群优化的组合测试用例生成方法

最小覆盖表生成是组合测试研究的关键问题。基于演化搜索的粒子群算法在生成覆盖表时能得到较优的结果,但其性能受配置参数的影响。针对此问题,将one-test-at-a-time策略和自适应粒子群算法相结合,以种群粒子优劣为依据对惯性权重进行自适应调整,使其在覆盖表生成上具有更强的适用能力。为进一步提升算法性能,构造了一个优先级度量函数用于度量每个组合的权值,优先选取权值最高的组合用于单条测试用例的生成。最后,编程实现该算法,并将其与原有粒子群算法在组合测试用例集生成上展开对比性实验分析,结果证实该算法在规模和执行时间上具有竞争力。     

基于马尔科夫决策过程的软件测试仿真与计算

提出了一个改进的马尔科夫决策过程的软件测试模型,应用交叉熵方法计算求解改进后的测试模型下的软件测试优化策略,得到最优测试剖面,使得平均测试费用最小.并对采用随机软件测试策略,原始的MDP模型软件测试策略和改进后的MDP模型软件测试策略的软件测试过程进了仿真.仿真结果表明,改进后的软件测试策略不仅能够大大降低期望测试费用,而且也减少了测试用例的使用数量,提高了软件测试的效率和有效性.     

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。     

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.     

3n镜像适应性随机测试算法

针对适应性随机测试中的边缘效应问题,借鉴镜像适应性随机测试(MART)的基本思想,在引入镜像距离的基础上,提出3n MART算法,通过改变候选用例与成功用例之间的距离判断策略,使其产生的用例在输入域中更均匀地分布。仿真实验结果证明,该算法可以较好地解决边缘效应问题,相比基于距离的适应性随机测试算法与MART算法,失效发现效率更高。     

Adaptive random testing based on distribution metrics

Random testing (RT) is a fundamental software testing technique. Adaptive random testing (ART), an enhancement of RT, generally uses fewer test cases than RT to detect the first failure. ART generates test cases in a random manner, together with additional test case selection criteria to enforce that the executed test cases are evenly spread over the input domain. Some studies have been conducted to measure how evenly an ART algorithm can spread its test cases with respect to some distribution metrics. These studies observed that there exists a correlation between the failure detection capability and the evenness of test case distribution. Inspired by this observation, we aim to study whether failure detection capability of ART can be enhanced by using distribution metrics as criteria for the test case selection process. Our simulations and empirical results show that the newly proposed algorithms not only improve the evenness of test case distribution, but also enhance the failure detection capability of ART.     

Adaptive Random Testing: The ART of test case diversity

Random testing is not only a useful testing technique in itself, but also plays a core role in many other testing methods. Hence, any significant improvement to random testing has an impact throughout the software testing community. Recently, Adaptive Random Testing (ART) was proposed as an effective alternative to random testing. This paper presents a synthesis of the most important research results related to ART. In the course of our research and through further reflection, we have realised how the techniques and concepts of ART can be applied in a much broader context, which we present here. We believe such ideas can be applied in a variety of areas of software testing, and even beyond software testing. Amongst these ideas, we particularly note the fundamental role of diversity in test case selection strategies. We hope this paper serves to provoke further discussions and investigations of these ideas.     

A uniform random test data generator for path testing

Path-oriented Random Testing (PRT) aims at generating a uniformly spread out sequence of random test data that execute a single control flow path within a program. The main challenge of PRT lies in its ability to build efficiently such a test suite in order to minimize the number of rejects (test data that execute another control flow path). We address this problem with an original divide-and-conquer approach based on constraint reasoning over finite domains, a well-recognized Constraint Programming technique. Our approach first derives path conditions by using backward symbolic execution and computes a tight over-approximation of their associated subdomain by using constraint propagation and constraint refutation. Second, a uniform random test data generator is extracted from this approximated subdomain. We implemented this approach and got experimental results that show the practical benefits of PRT based on constraint reasoning. On average, we got a two-order magnitude CPU time improvement over standard Random Testing on a set of paths extracted from classical benchmark programs.     

组合测试用例生成技术

组合测试是一种科学有效的软件测试方法,该方法旨在使用较少的测试用例有效地检测软件系统中各个因素以及它们之间的相互作用对系统产生的影响,实践证明其具有较高的错误检测能力。当前组合测试研究的热点之一是组合测试用例生成问题,即如何针对具体待测软件,在满足给定组合覆盖要求的前提下,生成规模尽可能小的测试用例集,以便在保证错误检测能力的前提下尽可能降低测试成本。从N维组合覆盖和变力度组合覆盖等两类不同的组合覆盖标准出发,简要介绍了迄今为止人们在组合测试用例生成领域所取得的研究成果,对现有的组合用例生成方法进行了分类和总结。此外,还对优先级、组合约束、错误定位等条件和应用场景下的组合测试用例生成技术进行了介绍。最后,分析了现有成果中存在的问题,并对该领域未来的研究方向进行了分析和讨论。     

Mirror adaptive random testing

Recently, adaptive random testing (ART) has been introduced to improve the fault-detection effectiveness of random testing for non-point types of failure patterns. However, ART requires additional computations to ensure an even spread of test cases, which may render ART less cost-effective than random testing. This paper presents a new technique, namely mirror ART, to reduce these computations. It is an integration of the technique of mirroring and ART. Our simulation results clearly show that mirror ART does improve the cost-effectiveness of ART.     

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.     

几种新型非易失存储器的原理及发展趋势

介绍几种有发展潜力的新型非易失存储器的原理,如铁电存储器、磁性随机存储器、相变存储器和阻变存储器等,并在性能方面作了对比,最后对存在的问题和发展趋势进行了分析。     

改进的局部ART测试算法研究

局部ART算法虽然克服了测试样本点与样本生成区域已测试顶点有可能距离很近的问题,但它对生成区域周围的已测试样本点对样本选择的影响考虑不足.本文针对局部ART算法的缺陷,提出了改进的局部ART算法,将生成区域周围一定范围内的已测试顶点都加入到临近已测试样本集中,在保持时间复杂度的同时,使测试样本的分布更加均匀.实验结果表明,改进的局部ART算法的错误检测能力得到了提升.