启发式遗传算法求解两两组合测试用例集

针对两两组合测试用例集的生成问题,在有效地结合了两两组合测试问题本身的特点基础上,提出了一种基于动态解空间的启发式遗传算法(HGA)。详细分析了测试用例生成过程,根据其解空间的动态变化在传统遗传算法中加入了启发算子,使得HGA算法可以快速地搜索出当前局部优化的测试用例。实验结果表明,HGA算法在完全覆盖参数两两组合的前提下有效地减少了测试用例的数量,并且具有较快的迭代速度。     

基于贡献度的两两组合测试用例生成算法

为在两两组合测试中获得近似最小的测试用例集,提出一种基于贡献度的两两组合测试用例自动生成算法。生成满足覆盖要求的有序配对集,根据有序配对集生成初始用例集,对初始用例集进行简约,获得测试用例集。实验结果表明,该算法生成的测试用例数目较少,算法效率较高。     

带参数约束的两两组合测试用例集生成方法

大多数待测软件系统的参数间存在约束关系,如果不考虑这些约束,会使测试用例集中的某些组合无效。为此,提出一种带参数约束的两两组合测试用例集生成方法。在采用回溯算法对解空间树进行遍历的同时,利用参数间的约束关系对树的枝叶进行裁剪,避免产生无效的测试用例。使用贪心算法补充生成测试用例,以满足两两组合覆盖标准。实验结果表明,该方法能减少测试用例生成数量,提高测试效率。     

基于组织进化粒子群优化的测试用例自动生成

针对组合测试用例生成问题的具体特点,结合组织进化思想及粒子群优化算法,设计了适合问题求解的编码方式及操作算子等,提出了一种基于组织进化粒子群优化的测试用例自动生成算法。该方法用于选择当前局部优化覆盖的测试用例,在此基础上构建满足两两覆盖的测试用例集。仿真实验表明,该方法能有效地降低测试用例数目。     

一种改进的两两组合测试用例生成方法

本文针对多因素二水平系统提出:将所有的测试数据表示为一棵二叉解空间树,用回溯法对二叉解空间树进行路径搜索来生成测试用例,然后使用贪心算法生成补充的测试用例,以满足两两组合覆盖标准。使用这种方法生成的测试数据集具有很高的覆盖率。     

关于两两测试的研究

在软件开发过程中软件测试的成本是很高的。但是，在许多情况下两两测试可用于减少测试的成本，并对于许多软件系统在实践中证明是实用和有效的。它要求对于系统的每一对输入参数，这两个参数有效值的每一个组合至少要被一个测试用例覆盖。介绍了两两测试的概念以及生成测试用例的两个算法。最后，对应用两两测试时应注意的一些问题进行了讨论．两两测试的许多思想可以扩展到t路测试．     

参数配对及n-way组合覆盖算法研究

组合测试是软件测试数据生成研究的一个重要领域,其中参数配对组合测试的应用最为广泛.对常用的参数配对组合覆盖方法进行了综述分析.目前主流的配对覆盖算法AETG和IPO所存在的主要问题是在确定水平取值时,具有盲目性和随机性,从而难以控制测试用例的规模.为此提出了改进的AETG算法和IPO算法,改进算法通过对参数进行预处理以及综合考虑各因素的水平组合等手段,对测试用例的规模进行控制,采用更加完备的方法尽早确定水平取值.为验证新算法的有效性,进行了仿真实验和实际测试,结果表明,改进算法所生成的测试用例数量要少于原算法,测试用例约减效果更为明显;测试用例数与配对数之间、测试用例数与因素水平数之间存在着某些规律性的联系,分析得出了一系列相关的结论.在配对覆盖的基础上,提出了遗传算法与AETG算法相结合的n-way组合覆盖算法,证明了其时间复杂性较已有算法得到了改善.     

带权值参数的两两组合测试用例集生成算法

通常对组合测试研究的重点是生成最小的测试用例集,但其中却很少涉及到带权值的参数。针对带权值参数的两两组合测试用例生成问题,提出一种基于逐参数( IPO)策略的带权值参数两两组合测试用例生成算法。对影响IPO策略性能的3个影响因子进行改进,包括待扩展参数的扩展次序、已有测试集的扩展次序和待扩展参数的取值选择。在扩展完所有参数后,对此时的测试集使用约简算法进一步简化,得到按测试用例权值和降序排列的测试集。实验结果表明,该算法不仅能减少测试用例的生成数量,而且能解决参数的权值问题,使其在实际应用中可以更有效地降低测试成本。     

基于动态调整简化粒子群优化的组合测试用例生成方法

优化的组合测试中的一个关键是生成的测试用例能够覆盖更多的组合,而粒子群算法在生成强组合覆盖用例方面有其独特的优势和能力。文中提出了一种基于动态调整简化粒子群优化的组合测试用例生成方法。该方法基于粒子群算法生成测试用例,结合混合的优先级one-test-at-a-time策略和基于动态调整的简化粒子群算法生成组合测试用例集,排除了速度因素对粒子优化过程的影响。定义了一个粒子收敛指标,以粒子群早熟收敛程度为依据来动态调整惯性权值,以防止粒子陷入局部最优和后期出现收敛速度慢的情况,从而提高粒子群算法所生成的覆盖表的覆盖组合能力。通过对比实验表明,基于动态调整的简化粒子群优化算法在用例规模和时间成本上具有一定的优势。     

相邻因素组合测试用例集的最优生成方法

软件系统是一个复杂的逻辑系统,有很多因素可能影响系统的正常运行,组合测试可以对这些因素及其相互作用可能对系统产生的影响进行检测.针对一类只在相邻因素间存在相互作用的系统,文中提出了相邻因素组合测试的概念,分别给出了相邻因素两两(二维)组合覆盖表、相邻因素N(N＞2)维组合覆盖表和多重维数相邻因素组合覆盖表的生成算法,并证明了3个算法均可以生成数量最优的相邻因素组合测试用例集.最后通过实际应用场景,分析了相邻因素组合测试的应用价值.     

一种平衡结构性测试和功能性测试的方法

分析了结构性测试和功能性测试方法及其优劣,提出了一种平衡结构性测试和功能性测试的模型方法,即定义-使用测试方法。该方法定义了介于全路径指标和全边之间的测试覆盖指标,提供了一种检查缺陷可能发生点的系统化方法。模拟实例结果表明,定义-使用测试方法能够显著提高发现缺陷的效率。     

Comparison of adaptive random testing and random testing under various testing and debugging scenarios

Adaptive random testing is an enhancement of random testing. Previous studies on adaptive random testing assumed that once a failure is detected, testing is terminated and debugging is conducted immediately. It has been shown that adaptive random testing normally uses fewer test cases than random testing for detecting the first software failure. However, under many practical situations, testing should not be withheld after the detection of a failure. Thus, it is important to investigate the effectiveness with respect to the detection of multiple failures. In this paper, we compare adaptive random testing and random testing under various scenarios and examine whether adaptive random testing is still able to use fewer test cases than random testing to detect multiple software failures. Our study delivers some interesting results and highlights a number of promising research projects. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal testing resource allocation during module testing considering cost, testing effort and reliability

Software reliability is one of the most important quality attributes of commercial software. During software testing, software reliability growth models (SRGMs) are commonly used to describe the phenomenon of failure occurrence and/or fault removal which consequently enhancements software reliability. Large software systems are developed by integrating a number of relatively small and independent modules, which are tested independently during module testing phase. The amount of testing resource available is limited which is desired to be consumed judiciously so as to optimize the testing process. In this paper we formulate a resource allocation problem of minimizing the cost of software testing under available amount of testing resource, given a reliability constraint. We use a flexible SRGM considering testing effort which, depending upon the values of parameters, can describe either exponential or S-shaped failure pattern of software modules. A systematic and sequential Algorithm is proposed to solve the optimization problem formulated. Numerical examples are given to illustrate the formulation and solution procedures. Sensitivity analysis is performed to examine the behavior of some parameters of SRGM with most significant influence.     

软件回归测试中的自动测试生成方法

软件回归测试一般使用现有的测试用例集进行测试,如何有效利用这些用例成为回归测试的关键。研究自动对现有测试用例集的扩展,包括自动改变测试用例的执行顺序、自动进行测试用例数目的增减等,提出基于现有测试用例随机生成新的测试用例集的方法。实验证明,该方法生成的测试文件能在原有环境下成功执行,能实现自动测试范围的扩大或缩小,以及测试时间的增加或减少,为软件回归测试提供了有用的自动化工具。     

Fair testing

In this paper we present a solution to the long-standing problem of characterising the coarsest liveness-preserving pre-congruence with respect to a full (TCSP-inspired) process algebra. In fact, we present two distinct characterisations, which give rise to the same relation: an operational one based on a De Nicola–Hennessy-like testing modality which we call should-testing, and a denotational one based on a refined notion of failures. One of the distinguishing characteristics of the should-testing pre-congruence is that it abstracts from divergences in the same way as Milner’s observation congruence, and as a consequence is strictly coarser than observation congruence. In other words, should-testing has a built-in fairness assumption. This is in itself a property long sought-after; it is in notable contrast to the well-known must-testing of De Nicola and Hennessy (denotationally characterised by a combination of failures and divergences), which treats divergence as catastrophic and hence is incompatible with observation congruence. Due to these characteristics, should-testing supports modular reasoning and allows to use the proof techniques of observation congruence, but also supports additional laws and techniques. Moreover, we show decidability of should-testing (on the basis of the denotational characterisation). Finally, we demonstrate its advantages by the application to a number of examples, including a scheduling problem, a version of the Alternating Bit-protocol, and fair lossy communication channels.     

Refusal testing

When manipulating concurrent processes it is desirable to suppress their internal details and to consider two processes to be equivalent if their external behaviours are equivalent. Following Milner and De Nicola & Hennessy we take this external equivalence to mean that an observer cannot tell the processes apart by testing their responses to the same stimuli. We introduce a form of testing (refusal testing) which is more powerful than that of De Nicola & Hennessy in that the observer not only tests whether a process will perform an action but is also allowed under certain circumstances to discover in a finite amount of time that the process will not perform an action. The equivalence associated with refusal testing is compared with De Nicola & Hennessy's testing equivalence and Milner's observation equivalence, and a sound and complete proof system is provided for refusal equivalence when applied to CCS processes.     

防火墙测试

防火墙测试在国内还是一个新的领域,测试方法和手段仍不完善,专门测试机构仍在探索科学的测试方法和测试工具,通过测试,可以帮助厂商完善防火墙系统,推动防火墙的应用。我们特别约请赛迪评测的安全专家何军先生对防火墙测试进行全面阐释。