基于Markov链路径使用模型的软件统计测试

探讨了建立基于路径使用的Markov链模型来分析模块内部代码结构的统计测试方法的可能性。提出将统计理论用于软件的结构测试，从理论上得出软件的结构可靠性结论。提出具有有限状态、离散时间序列的Markov链作为程序的使用模型和测试模型，使用Kullback判别式作为测试链收敛到使用链的判断准则，从理论上证明了测试链到使用链收敛的必然性。理论分析和初步的实验证明该方法是可行和有前途的。     

基于状态空间剪枝的软件测试数据扩增算法

由于软件测试数据待测行为段序列连接存在冗余,导致目标路径覆盖率降低,提出基于状态空间剪枝的软件测试数据扩增算法.通过并发无关行为段在软件测试内的位置实施分类,依据分类结果采用状态空间剪枝算法,缩减状态空间的规模后,采用测序序列生成算法采用状态节点投影,对所有待测行为段实施操作和判断,按照状态空间实施全序列连接操作,生成全覆盖、无冗余的测试序列;采用自适应粒子群优化算法,设置初始参数、初始种群,判断终止条件,在扩增的测试数据覆盖目标路径时,输入覆盖的测试序列数据完成软件测试数据扩增.实验结果表明,上述算法在软件测试数据扩增效率高,耗时低,平均运行时间低至0.51s,目标路径覆盖率高达到1.0,并且后期的目标路径覆盖率平稳.     

基于受控Markov链的软件自适应测试策略

基于简化的受控Markov链软件自适应测试模型大多是研究如何以最小的期望成本检测并移除所有的缺陷,并在构建模型时对部分条件进行特殊化和理想化处理.针对受控Markov链软件测试模型适用范围小、效率低的缺陷,在软件控制论思想基础上,对制约条件进行了一系列新的转换,提出一种改进的、资源约束的受控Markov链模型,该模型能够在高效性、复杂性和适用性3方面达到一个平衡.根据该模型设计一种新的软件缺陷优化测试策略,再通过参数估计对优化测试策略进行在线调整的方法,以构造软件自适应测试策略.为了证明其有效,利用该模型得到的新的软件自适应测试策略进行仿真实验,进一步得到了有效结果.     

测试资源受约束的安全关键软件加速测试方法

基于马尔可夫链使用模型提出了一种针对安全关键软件测试资源受约束的启发式加速测试方法.该方法利用一种新的随机优化技术--交叉熵方法,以软件投放后软件失效风险损失最小为目标,基于失效风险损失通过修正操作剖面,自动生成测试数据集.实验结果表明该方法能有效地降低软件失效风险,提高测试效率,是一种快速有效的加速测试方法.     

浅谈面向对象软件类的测试

面向对象软件测试与传统的软件测试区别不大,只有类和类簇的测试才体现了面向对象软件测试的特点,而两者之间又以类的测试最为关键。本文通过对状态机模型生成类的测试序列的方法和代码实现以及基于状态测试法的测试数据生成的问题的探讨,认为基于状态的测试方法和基于代数规约的测试方法（ASTOOT方法）将是类测试的主要的发展方向,其余方法大多只能以辅助的面貌出现。     

浅谈面向对象软件类的测试

面向对象软件测试与传统的软件测试区别不大,只有类和类簇的测试才体现了面向对象软件测试的特点,而两者之间又以类的测试最为关键。本文通过对状态机模型生成类的测试序列的方法和代码实现以及基于状态测试法的测试数据生成的问题的探讨,认为基于状态的测试方法和基于代数规约的测试方法(ASTOOT方法)将是类测试的主要的发展方向,其余方法大多只能以辅助的面貌出现。     

测试数据生成技术在软件故障诊断中的应用

随着软件在武器装备中应用范围的日益扩大,软件故障诊断问题日显突出。文中针对软件测试中测试数据生成问题作了重点研究,采用了遗传算法解决按路径生成测试数据的问题,构建了基于遗传算法的测试数据生成系统模型,并对系统模型进行了实例验证。通过数据证明了遗传算法在测试数据生成中的优越性。     

基于路径引导的回归测试用例集扩增方法

为了全面测试演化软件,回归测试通常需要生成新的测试用例。concolic测试是一种沿着具体执行路径进行符号执行的软件验证技术,通过生成测试数据来执行程序的所有可行路径。回归测试中,由于concolic测试关注于程序本身,没有利用已有测试用例和软件演化信息,导致生成大量无效测试数据,浪费资源和时间。为解决此问题,提出一种基于路径引导的回归测试用例集扩增方法。该方法将目标路径作为引导,根据软件演化信息选择有利于覆盖目标路径的测试用例,利用已有测试用例跳过重叠初始子路径,对后续目标子路径进行concolic测试并生成覆盖目标路径的测试数据。案例分析表明,本文方法相比传统concolic测试,本方法在覆盖程序可行路径的同时,可有效减少concolic测试路径,提高测试数据生成效率。     

测试数据生成技术在软件故障诊断中的应用

随着软件在武器装备中应用范围的日益扩大，软件故障诊断问题日显突出。文中针对软件测试中测试数据生成问题作了重点研究，采用了遗传算法解决按路径生成测试数据的问题，构建了基于遗传算法的测试数据生成系统模型，并对系统模型进行了实例验证。通过数据证明了遗传算法在测试数据生成中的优越性。     

基于关键点路径的快速测试用例自动生成方法

测试数据的自动生成,是提高软件测试效率的重要手段.从软件测试工程实践的角度提出快速生成测试数据的完整模型,更有利于提高测试数据生成效率.为此:(1)提出关键点路径表示法,以得出待测程序的理论路径数,并快速确定已覆盖路径的邻近路径;(2)用随机生成的数据运行简化后的插装程序,得到部分测试数据;(3)将理论路径分成易覆盖路径、难覆盖路径和不可行路径;(4)根据已覆盖路径及其测试数据提供的信息,使用遗传算法生成难覆盖路径的测试数据.仿真实验结果表明了所提方法的有效性.     

A Markov chain model for statistical software testing

Statistical testing of software establishes a basis for statistical inference about a software system's expected field quality. This paper describes a method for statistical testing based on a Markov chain model of software usage. The significance of the Markov chain is twofold. First, it allows test input sequences to be generated from multiple probability distributions, making it more general than many existing techniques. Analytical results associated with Markov chains facilitate informative analysis of the sequences before they are generated, indicating how the test is likely to unfold. Second, the test input sequences generated from the chain and applied to the software are themselves a stochastic model and are used to create a second Markov chain to encapsulate the history of the test, including any observed failure information. The influence of the failures is assessed through analytical computations on this chain. We also derive a stopping criterion for the testing process based on a comparison of the sequence generating properties of the two chains     

基于使用模型的嵌入式软件测试用例生成研究

基于Markov链使用模型的软件统计测试是产生高效测试用例,实现软件可靠性定量评估的有效方法。介绍了基于使用模型的统计测试方法,论述了使用模型的概念和特点,以及从使用模型中可以计算出的静态参数和它们在统计测试和软件开发中所起的作用。提出Markov链使用模型用于嵌入式软件的测试,从理论上得到嵌入式软件的可靠性。具体阐述了嵌入式软件的Markov链使用模,型自动生成测试模型的方法。同时使用改进的Kullback判别式探讨测试的可靠性问题,从理论上证明了测试链到使用链收敛的必然性。理论分析和初步的实例证明该方法是可行的和有前途的。     

基于确定有限状态机的测试输入序列选取

有限状态机可以精确地刻画软件系统或其子系统的行为，其在软件建模中被广泛应用，基于有限状态机的测试问题得到了广泛的研究，其中WP方法由于限制条件较少，并且其可以达到较高的错误覆盖度，因此被广泛使用，但当有限状态机的实现中的状态数目的估计值m较大时，产生的测试输入序列相对较少的测试输入序列数目，可以提高测试效率，同时本文对Wp方法和R-Wp方法产生的测试输入序列数目进行了讨论，并证明了该方法与Wp方法相同的错误覆盖能力。     

考虑测试工作量和故障检测率的软件可靠性模型

针对软件可靠性模型中对故障检测率行为描述的不足,考虑软件固有的故障检测率是一个随时间增加的减函数,以及测试人员的学习能力是一个随时间增加呈S型的增函数,两者共同决定了故障检测率是一个随时间增加的先增后减的函数,于是提出改进的软件可靠性模型。在改进模型的基础上,考虑测试工作量对可靠性的影响,软件可靠性模型得到了进一步的改善。利用公开发表的失效数据集对改进的模型进行比较和验证,实验数据证明改进的模型具有很好的效果。     

Generating test data for both path coverage and fault detection using genetic algorithms

The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current stud- ies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolution- ary algorithm is employed to solve the formulated model, and several types of fault detection methods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary opti- mization method in the following three aspects： the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.     

航天软件测试模型构建与应用

针对航天软件高可靠性的特点,构建一种输入与输出模型测试方案,提出基于覆盖测试算法的测试方法,测试覆盖算法主要包含三种功能测试方法和三种结构测试方法。功能测试主要使用基于边界的方法、定义等价类、使用决策表分析三种方法。结构测试主要使用基于路径的测试、数据流测试、片测试三种方法。实例验证,该方法清晰明了、便于发现航天软件缺陷、降低航天软件开发风险与代价以及保证航天软件质量。     

基于Markov链使用模型的软件可靠性测评方法研究

基于Markov链使用模型的软件统计测试是产生高效测试用例,实现软件可靠性定量评估的有效方法.论述了该方法中使用链与测试链的构建技术,基于使用模型的统计分析计算,基于测试链的软件可靠性评估,操作可靠性和使用可靠性评估,以及它们在实际应用中需要注意的若干问题.     

A time/structure based software reliability model

The past 20 years have seen the formulation of numerous analytical software reliability models for estimating the reliability growth of a software product. The predictions obtained by applying these models tend to be optimistic due to the inaccuracies in the operational profile, and saturation effect of testing. Incorporating knowledge gained about some structural attribute of the code, such as test coverage, into the time-domain models can help alleviate this optimistic trend. In this paper we present an enhanced non-homogeneous Poisson process (ENHPP) model which incorporates explicitly the time-varying test-coverage function in its analytical formulation, and provides for defective fault detection and test coverage during the testing and operational phases. It also allows for a time varying fault detection rate. The ENHPP model offers a unifying framework for all the previously reported finite failure NHPP models via test coverage. We also propose the log-logistic coverage function which can capture an increasing/decreasing failure detection rate per fault, which cannot be accounted for by the previously reported finite failure NHPP models. We present a methodology based on the ENHPP model for reliability prediction earlier in the testing phase. Expressions for predictions in the operational phase of the software, software availability, and optimal software release times subject to various constraints such as cost, reliability, and availability are developed based on the ENHPP model. We also validate the ENHPP model based on four different coverage functions using five failure data sets. This revised version was published online in June 2006 with corrections to the Cover Date.     

A practical model‐based statistical approach for generating functional test cases: application in the automotive industry

With the growing complexity of industrial software applications, industrials are looking for efficient and practical methods to validate the software. This paper develops a model‐based statistical testing approach that automatically generates online and offline test cases for embedded software. It discusses an integrated framework that combines solutions for three major software testing research questions: (i) how to select test inputs; (ii) how to predict the expected results of a test; and (iii) when to stop testing software. The automatic selection of test inputs is based on a stochastic test model that accounts for the main particularity of embedded software: time sensitivity. Software test practitioners may design one or more test models when they generate random, user‐oriented, or fault‐oriented test inputs. A formal framework integrating existing and appropriate specification techniques was developed for the design of automated test oracles (executable software specifications) and the formal measurement of functional coverage. The decision to stop testing software is based on both test coverage objectives and cost constraints. This approach was tested on two representative case studies from the automotive industry. The experiment was performed at unit testing level in a simulated environment on a host personal computer (automatic test execution). The two software functionalities tested had previously been unit tested and validated using the test design approach conventionally used in the industry. Applying the proposed model‐based statistical testing approach to these two case studies, we obtained significant improvements in performing functional unit testing in a real and complex industrial context: more bugs were detected earlier and in a shorter time. Copyright © 2012 John Wiley & Sons, Ltd.