基于蚁群算法优化软件测试策略

提高软件测试的缺陷检测能力,有效降低测试成本是软件测试优化研究中的关键问题。基于软件测试的Markov决策模型,以降低软件测试成本,提高测试的缺陷检测能力为目标,运用蚁群算法给出一种优化测试剖面的学习策略,将所得到的最优测试剖面用于优化软件测试。实验结果表明运用蚁群算法的学习策略要远优于随机测试策略,能显著降低测试成本和提高缺陷检测能力,是软件测试优化启发式方法的一个重要补充。     

基于缺陷分析的软件测试有效性评估方法

为利用软件测试活动检测到的缺陷信息进行测试活动的有效性评估,改进软件测试活动,提出一种基于缺陷度量和缺陷基线的测试有效性评估方法.以正交缺陷分类方法为基础建立软件缺陷的分类框架,研究缺陷触发特征、缺陷类型统计信息与测试活动的关联,建立缺陷关联基线.通过分析实际缺陷度量与缺陷基线的偏离情况,评估测试活动的有效性,明确测试活动的改进方向.实例分析表明,该模型能够便捷地应用于测试活动有效性的定性评估.     

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Markov决策过程用交叉熵方法优化软件测试

研究了待测软件某些参数已知的条件下,以最小化平均测试费用为目标的软件测试优化问题.将软件测试过程处理成马尔可夫(Markov)决策过程,给出了软件测试的马尔可夫决策模型,运用交叉熵方法,通过一种学习策略获得软件测试的最优测试剖面,用于优化软件测试.模拟结果表明,学习策略给出的测试剖面要优于随机测试策略,检测和排除相同数目的软件缺陷,学习策略比随机测试能够显著地减少测试用例数,降低测试成本,提高缺陷检测效率.     

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.     

嵌入式软件路径覆盖测试的研究

路径覆盖是软件测试中一种十分重要的方法,它使程序的每个分支至少执行一次;针对嵌入式软件测试的特点,提出了嵌入式软件路径覆盖测试的策略,通过模拟测试通用型智能水量计量仪C430主控程序的各个步骤,运用基本路径插桩策略分析计算插桩探针的位置、个数,统计计算整个探针的覆盖面,构造基本路径和实际程序执行路径;测试覆盖率达到71.1%,取得了一定的效果,对增强软件测试方案设计的系统性,提高软件测试质量和效率,起到了较好的作用.     

一种基于测试状态的回归测试方法

软件测试在软件生命周期中是一个非常重要的过程,而回归测试则又在软件测试中占有极其重要的地住。本文提出的RTSPS方法综合考虑了测试状态即测试频次、测试成本、错误检测率要求等多种因素来选择合适的回归测试策略,并对测试用例进行优先排序,可以有效地提高测试效率。     

支持协同测试的软件测试信息管理系统

提出支持协同测试的通信设备系统软件测试信息管理系统结构,研究了测试需求、测试用例和故障之间映射关系的测试信息关联模型,分析归纳了通信设备系统软件测试流程,提出的测试用例编号方法和协同测试信息模型可以有支持系统软件多版本的协同测试.以testDirector为基础,实现了支持协同测试的软件测试信息管理系统.     

基于软件层次化模型的软件测试数据生成

针对基于Markov链模型的软件测试技术在测试数据生成时不考虑软件的结构信息,生成的测试数据集对代码路径的覆盖能力以及缺陷检测能力都较低的问题,将统计测试与基于Markov链模型的测试相结合,提出了一种新的软件测试模型——软件层次化模型。该模型涵盖了软件与外部环境之间的交互,同时描述了软件内部结构信息。还给出了该模型测试数据集的生成算法：首先生成符合使用情况的测试序列,然后为测试序列生成覆盖软件内部结构的输入数据。通过针对示例软件的实验结果表明,与基于Markov链模型的测试方法对比,基于软件层次化模型的测试在满足软件测试充分性要求的同时,提高了测试数据集的代码路径覆盖能力和缺陷检测能力。     

用于黑盒测试的测试用例优先级改进算法

为了在源代码不可见的黑盒环境下提高软件测试效率,研究了通过调整测试用例执行顺序的测试用例优先级方法在黑盒测试中的应用.针对已有的用于黑盒测试优先级方法的不足,提出了改进的基于黑盒测试的优先级方法.以提高错误检测率为目标,该方法通过结合两种用于不同情况下的优先级调整策略和使用动态方法代替静态方法生成优先级步调调整值对原方法提出了两处改进.仿真实验结果表明,该算法有效且可行,两处改进均能有效地提高测试集的错误检测率,同时使用改进效果尤为明显.     

一种软件缺陷模式自定制方法

软件缺陷是导致软件不可靠的根本原因,提高软件可靠性的关键在于减少软件缺陷。基于缺陷模式的代码分析技术根据预先设定好的缺陷模式对待测代码进行缺陷分析,这种缺陷分析具有使用简单、查找速度快等优点,是近年来静态代码分析技术中发展比较迅速的新技术。但是目前基于这种分析技术的大多数工具并没有为用户提供足够易用、高效的扩展方式以扩充其缺陷检测能力。本文出了一种支持用户定制语法相关缺陷模式的测试方法及系统,该方法能够让用户根据实际情况需要对缺陷模式进行定制,目的是检测程序代码中是否包含语法相关的缺陷。     

软件关联缺陷的一种检测方法

软件中的关联缺陷是一种比较普遍的现象,某些缺陷的存在与否可能导致其他缺陷检测率的变化.软件关联缺陷是造成软件失效关联的根源.给出了关联缺陷的定义,通过一个软件实例验证了缺陷的关联关系,提出了一种缺陷放回的测试方法用来剔除关联缺陷,并通过实验数据分析了缺陷放回方法的能力和效率.实验数据表明,该方法能有效检测软件关联缺陷.     

Integrating in-process software defect prediction with association mining to discover defect pattern

Rather than detecting defects at an early stage to reduce their impact, defect prevention means that defects are prevented from occurring in advance. Causal analysis is a common approach to discover the causes of defects and take corrective actions. However, selecting defects to analyze among large amounts of reported defects is time consuming, and requires significant effort. To address this problem, this study proposes a defect prediction approach where the reported defects and performed actions are utilized to discover the patterns of actions which are likely to cause defects. The approach proposed in this study is adapted from the Action-Based Defect Prediction (ABDP), an approach uses the classification with decision tree technique to build a prediction model, and performs association rule mining on the records of actions and defects. An action is defined as a basic operation used to perform a software project, while a defect is defined as software flaws and can arise at any stage of the software process. The association rule mining finds the maximum rule set with specific minimum support and confidence and thus the discovered knowledge can be utilized to interpret the prediction models and software process behaviors. The discovered patterns then can be applied to predict the defects generated by the subsequent actions and take necessary corrective actions to avoid defects.The proposed defect prediction approach applies association rule mining to discover defect patterns, and multi-interval discretization to handle the continuous attributes of actions. The proposed approach is applied to a business project, giving excellent prediction results and revealing the efficiency of the proposed approach. The main benefit of using this approach is that the discovered defect patterns can be used to evaluate subsequent actions for in-process projects, and reduce variance of the reported data resulting from different projects. Additionally, the discovered patterns can be used in causal analysis to identify the causes of defects for software process improvement.     

A neural network approach for optimal software testing and maintenance

In this paper, we propose a neural network-based model for optimal software testing and maintenance policy, where the software testing environment and the operational environment are characterized by an environmental factor. We also present a systematic study for defect detection and correction processes. In our proposed approach, we consider the logistic growth curve model and the constant correction time for defect prediction. Then, we estimate the jointly optimal software testing period and maintenance limit via minimization of a software cost function that takes into account the environmental factor and the imperfect fault removal. More precisely, the total expected cost is formulated via a discrete-type software reliability model based on the difference between operational environments, imperfect defect removal, and defect correction process. Experimental results on a real software data set are presented to demonstrate the effectiveness of the proposed approach in defect prediction as well as in determining the jointly optimal testing period and planned maintenance limit.     

A composite complexity approach for software defect modelling

An approach is proposed to develop defect models for software components based on a categorical multivariate regression analysis. This modelling technique is useful when the software components are sufficiently small so that the assumption of a continuous normally distributed defect distribution is not valid. Library unit aggregations from five Ada projects are analysed to yield a composite complexity measure which is a function of both software complexity characteristics and development environment characteristics. The probabilities of various numbers of defects are derived from this composite complexity measure. The probability distributions are used to calculate subsystem level defects which are then compared to the actual defects.     

电压频控中抗强干扰软件关联缺陷检测

针对传统电压频控软件缺陷检测技术未考虑软件缺陷分类,存在检测精度低的问题,提出一种电压频控中抗强干扰软件关联缺陷检测技术。对软件关联缺陷检测原理进行分析,采用判别函数对待测软件样本进行识别,引入统计模式识别算法处理软件原始数据,依据关联缺陷概率分配,确定关联缺陷类别,计算缺陷特征值,利用贝叶斯分类器对关联缺陷进行划分,完成抗强干扰软件关联缺陷的分类,从而实现关联缺陷的高精度检测。实验结果表明,该检测技术对软件缺陷进行准确分类,在保证强抗干扰性的前提下,有效提高了检测精度。     

An analysis of several software defect models

Results are presented of an analysis of several defect models using data collected from two large commercial projects. Traditional models typically use either program matrices (i.e. measurements from software products) or testing time or combinations of these as independent variables. The limitations of such models have been well-documented. The models considered use the number of defects detected in the earlier phases of the development process as the independent variable. This number can be used to predict the number of defects to be detected later, even in modified software products. A strong correlation between the number of earlier defects and that of later ones was found. Using this relationship, a mathematical model was derived which may be used to estimate the number of defects remaining in software. This defect model may also be used to guide software developers in evaluating the effectiveness of the software development and testing processes     

A critique of software defect prediction models

Many organizations want to predict the number of defects (faults) in software systems, before they are deployed, to gauge the likely delivered quality and maintenance effort. To help in this numerous software metrics and statistical models have been developed, with a correspondingly large literature. We provide a critical review of this literature and the state-of-the-art. Most of the wide range of prediction models use size and complexity metrics to predict defects. Others are based on testing data, the “quality” of the development process, or take a multivariate approach. The authors of the models have often made heroic contributions to a subject otherwise bereft of empirical studies. However, there are a number of serious theoretical and practical problems in many studies. The models are weak because of their inability to cope with the, as yet, unknown relationship between defects and failures. There are fundamental statistical and data quality problems that undermine model validity. More significantly many prediction models tend to model only part of the underlying problem and seriously misspecify it. To illustrate these points the Goldilock's Conjecture, that there is an optimum module size, is used to show the considerable problems inherent in current defect prediction approaches. Careful and considered analysis of past and new results shows that the conjecture lacks support and that some models are misleading. We recommend holistic models for software defect prediction, using Bayesian belief networks, as alternative approaches to the single-issue models used at present. We also argue for research into a theory of “software decomposition” in order to test hypotheses about defect introduction and help construct a better science of software engineering     

Optimal and adaptive testing for software reliability assessment

Optimal software testing is concerned with how to test software such that the underlying testing goal is achieved in an optimal manner. Our previous work shows that the optimal testing problem for software reliability growth can be treated as closed-loop or feedback control problem, where the software under test serves as a controlled object and the software testing strategy serves as the corresponding controller. More specifically, the software under test is modeled as controlled Markov chains (CMCs) and the control theory of Markov chains is used to synthesize the required optimal testing strategy. In this paper, we show that software reliability assessment can be treated as a feedback control problem and the CMC approach is also applicable to dealing with the optimal testing problem for software reliability assessment. In this problem, the code of the software under test is frozen and the software testing process is optimized in the sense that the variance of the software reliability estimator is minimized. An adaptive software testing strategy is proposed that uses the testing data collected on-line to estimate the required parameters and selects next test cases. Simulation results show that the proposed adaptive software testing strategy can really work in the sense that the resulting variance of the software reliability estimate is much smaller than that resulting from the random testing strategies. The work presented in this paper is a contribution to the new area of software cybernetics that explores the interplay between software and control.