基于AETG算法的两两组合测试用例生成方法

对两两组合测试用例生成算法进行研究,在AETG算法的基础上进行改进,主要改进了AETG算法的参数排序过程。计算每个参数当前在未覆盖配对集中出现的次数,综合考虑其整体出现的次数以及单个取值出现的次数决定待扩展的参数序列。实验结果表明,该方法在缩短时间开销的基础上进一步减少了待测系统用例集的规模,当参数取值逐渐增加时,其优势更加明显。     

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。     

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。     

一种基于粒子群优化的成对组合测试算法框架

提出一种基于粒子群优化的成对组合测试用例集生成算法框架.在生成测试用例时,该框架采用粒子群优化尝试生成强组合覆盖能力的测试用例,并研究了搜索空间、适应值函数和启发式的合理设定;在构造组合测试用例集时,以上述测试用例生成算法为基础,提出两种策略:一种基于one-test-at-a-time,另一种基于类IPO.编程实现该算法框架,并通过实证研究分析了算法框架中不同设定对组合测试用例集规模的影响;最后,与现有的经典方法在组合测试用例集生成规模和算法执行时间上进行了比较.最终结果表明,该算法具有竞争力.     

一种组合测试用例生成的可配置混合算法

组合测试是一种经过实践证明的科学有效的测试方法,其研究重点之一是组合测试用例集的生成算法。基于参数顺序渐进扩充策略IPO(In-Parameter-Order)是其中一种具有代表性的通用算法,其优势在于水平扩充算法的可选择性和测试用例集的可扩展性。算法在提取影响IPO策略效果的参数的基础上,给出可配置的IPO策略;采用遗传算法(Genctic-Algorithm)配置IPO策略中的水平扩充,得到新的混合算法IPO_GA。通过实验对可配置IPO策略中各个参数对算法的影响进行了对比分析;将IPO_ GA与部分已有算法进行了比较,结果表明在水平扩充过程中染色体较短时,IPO_GA效果较好;在解空间规模过大而导致染色体较长时,IPO_GA效果略差。     

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。     

基于接口参数的列控中心测试用例生成算法

在接口参数两两组合全面覆盖理论的基础上,提出一种基于树型结构的改进测试用例生成算法。该算法综合考虑外部接口参数和取值组合所产生的系统影响,具有一定的通用性及稳定性,并且在时间复杂度及空间复杂度上较以往的算法都有所改进。算法在CTCS2级列控中心的接口测试中取得了很好的效果,测试质量和测试效率均得到提高。     

基于One-test-at-a-time策略的可变力度组合测试用例生成方法

组合测试可以有效地检测软件系统中由各个因素间交互作用所引发的软件故障.但传统的组合测试方法对系统中各因素之间的实际交互关系考虑不足,难以有效处理交互力度不统一的情况,进而可能导致测试用例的冗余和检错能力的降低.针对该问题,应在充分考虑因素间实际交互关系的基础上,使用可变力度组合测试方法,从而实现对于因素间实际交互关系的覆盖.为此,文中针对一种新的可变力度组合测试模型,提出了两种基于one-test-at-a-time策略的可变力度组合测试用例集生成算法.实验表明,相对于已有的具备类似功能的测试用例生成算法和工具,文中提出的算法在测试用例集规模和算法运行时间上均具备一定优势,并可适用于固定力度组合测试、可变力度组合测试等不同测试模型.     

基于蚁群算法的软件接口测试用例生成

目前存在的自动化生成接口测试用例的方法有参数配对覆盖法、基于测试依据集的测试用例生成法等,这些算法在用例有效性与耗费资源方面没有足够优势,鉴于此提出基于蚁群方法的软件接口测试用例生成算法,对蚁群算法应用的前提、测试数据生成方法、测试用例生成方法等进行研究。实验分析了算法的优势和不足,提出了有待改进的部分。     

基于输入输出关系的局部组合测试技术

主要针对软件测试中黑盒测试时测试用例集过大,以及测试效率低的问题,提出了一种有用有效的测试用例集生成设计方法.该方法根据待测系统参数的输入输出关系,对输入参数进行分组生成输入参数的组合关系集,再仅对组合关系集中每组输入变量生成两两组合覆盖测试用例集合,然后进行水平拼接生成最终的用于待测系统测试的测试用例集合.实验结果表明,该方法不仅能有效地减少了测试用例数目,而且还能够保持了原来测试检错能力,从而提高测试效率.     

The AETG system: an approach to testing based on combinatorialdesign

This paper describes a new approach to testing that uses combinatorial designs to generate tests that cover the pairwise, triple, or n-way combinations of a system's test parameters. These are the parameters that determine the system's test scenarios. Examples are system configuration parameters, user inputs and other external events. We implemented this new method in the AETG system. The AETG system uses new combinatorial algorithms to generate test sets that cover all valid n-way parameter combinations. The size of an AETG test set grows logarithmically in the number of test parameters. This allows testers to define test models with dozens of parameters. The AETG system is used in a variety of applications for unit, system, and interoperability testing. It has generated both high-level test plans and detailed test cases. In several applications, it greatly reduced the cost of test plan development     

The density algorithm for pairwise interaction testing

There are many published algorithms for generating interaction test suites for software testing, exemplified by AETG, IPO, TCG, TConfig, simulated annealing and other heuristic search, and combinatorial design techniques. Among these, greedy one‐test‐at‐a‐time methods (such as AETG and TCG) have proven to be a reasonable compromise between the needs for small test suites, fast test‐suite generation, and flexibility to accommodate a variety of testing scenarios. However, such methods suffer from the lack of a worst‐case logarithmic guarantee on test suite size, while methods that provide such a guarantee at present are less efficient or flexible, or do not produce test suites that are competitive in size for practical testing scenarios. In this paper, a new algorithm establishes that efficient, greedy, one‐test‐at‐a‐time methods can indeed produce a logarithmic worst‐case guarantee on the test suite size. In addition, this can be done while still producing test suites that are of competitive size, and in a time that is comparable to the published methods. It is deterministic, guaranteeing reproducibility. It generates only one candidate test at a time, permits users to ‘seed’ the test suite with specified tests, and allows users to specify constraints of combinations that should be avoided. Further, statistical analysis examines the impact of five variables used to tune this density algorithm for execution time and test suite size: weighting of density for factors, scaling of density, tie‐breaking, use of multiple candidates, and multiple repetitions using randomization. Copyright © 2007 John Wiley & Sons, Ltd.     

基于DCS系统组件的变力度组合测试方法

系统组件间的交互故障及功能失效是导致核电厂数字化仪控系统（DCS）故障的主要原因,传统组合测试能有效检测组件之间交互作用,但存在测试数据冗余、检测能力下降等问题。提出一种基于逐参数扩展（IPO）策略的变力度组合测试方法IPO_VD。根据DCS系统组件开发的特性,从待测组件间实际交互关系,对水平扩展过程中参数的取值选择进行改进。实验结果表明,相比固定力度下的IPO算法,IPO_VD算法在测试数据规模、覆盖率以及检错能力上均有一定优势。在减小测试数据规模的同时实现对组件间实际交互关系的全覆盖。     

基于两两覆盖准则的遗传算法测试用例生成

软件参数的组合测试是发现参数组合问题的重要方法,但是参数组合测试面临着组合爆炸问题,成对测试可以有效降低测试成本。提出了一种基于遗传算法的成对测试生成方法,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成对测试基准的测试用例套,结果表明该方法能在不降低测试覆盖精度的情况下有效降低了测试用例数量。     

一种基于改进遗传算法的路径测试用例生成方法

采用遗传算法求解路径覆盖的测试用例生成问题是软件测试自动化的研究热点。针对传统标准遗传方法搜索测试用例易产生早熟收敛和收敛速度较慢的不足,设计了自适应的交叉算子和变异算子,提高了算法的全局寻优能力。基于动态生成算法框架,通过程序静态分析,考虑了分支嵌套深度的影响,结合层接近度和分支距离法,提出一种新的适应度函数。实验结果表明,该算法在面向路径的测试用例生成上优于传统方法,提高了测试效率。     

覆盖表生成的禁忌搜索算法

组合测试可以有效检测待测系统中由参数间交互作用而引发的故障.在其30多年的发展过程中,覆盖表生成一直是关键问题之一,相关研究文献已达200多篇.作为一种有效的覆盖表生成算法,已有的禁忌搜索算法在所生成的覆盖表规模上具备一定的优势,但其解的质量和运算速度仍有提升空间;同时,这些算法实际应用能力较差,既不支持约束处理,也无法生成可变力度覆盖表.针对以上问题,提出了一种禁忌搜索算法.该算法从3个方面对已有的算法进行了改进：1）算法参数配置调优分pair-wise和爬山两阶段进行,确保使用较少配置条数最大程度击中最优配置,进一步提高算法生成覆盖表的规模;2）进行算法并行化,加速算法生成覆盖表的速度;3）增加约束处理和变力度处理,使算法可适应多种测试场景.实验结果表明,该算法在固定力度、变力度、带约束等多种类型覆盖表的规模上都具有一定优势,同时,并行化使算法平均加速2.6倍左右.     

Test suite reduction methods that decrease regression testing costs by identifying irreplaceable tests

ContextIn software development and maintenance, a software system may frequently be updated to meet rapidly changing user requirements. New test cases will be designed to ensure the correctness of new or modified functions, thus gradually increasing the test suite’s size. Test suite reduction techniques aim to decrease the cost of regression testing by removing the redundant test cases from the test suite and then obtaining a representative set of test cases that still yield a high level of code coverage.ObjectiveMost of the existing reduction algorithms focus on decreasing the test suite’s size. Yet, the differences in execution costs among test cases are usually significant and it may take a lot of execution time to run a test suite consisting of a few long-running test cases. This paper presents and empirically evaluates cost-aware algorithms that can produce the representative sets with lower execution costs.MethodWe first use a cost-aware test case metric, called Irreplaceability, and its enhanced version, called EIrreplaceability, to evaluate the possibility that each test case can be replaced by others during test suite reduction. Furthermore, we construct a cost-aware framework that incorporates the concept of test irreplaceability into some well-known test suite reduction algorithms.ResultsThe effectiveness of the cost-aware framework is evaluated via the subject programs and test suites collected from the Software-artifact Infrastructure Repository — frequently chosen benchmarks for experimentally evaluating test suite reduction methods. The empirical results reveal that the presented algorithms produce representative sets that normally incur a low cost to yield a high level of test coverage.ConclusionThe presented techniques indeed enhance the capability of the traditional reduction algorithms to reduce the execution cost of a test suite. Especially for the additional Greedy algorithm, the presented techniques decrease the costs of the representative sets by 8.10–46.57%.     

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.