星敏感器软件可靠性增长方法

可靠性是星敏感器软件指标之一.由于软件规模增大和复杂性的增加,软件可能存在许多缺陷,采用软件可靠性增长的方法,可以根除故障产生的原因,从而逐步提高可靠性.采用这一思路,对星敏感器软件可靠性增长的实现途径进行了讨论.     

一种基于输入域的非参数软件可靠性评估模型

通过分析输入域软件可靠性模型和时间域软件可靠性模型的特点,建立一种基于输入域的非参数软件可靠性评估模型,从而克服一般输入域模型评估精度较差、无法预测的缺点.同时提出了基于非参数统计的方法来估计缺陷数和软件失效概率,从而为利用普通软件测试所获得测试数据进行软件可靠性评估提供了一种解决途径,实例验证表明了该评估模型可以较好地对软件可靠性进行评估,给出缺陷数和软件可靠性的合理估计,其估计精度不低于较好的时间域模型.     

面向多用户软件可靠性评估和测试方案研究

软件可靠性估计是软件可靠性研究的重要问题之一。本文提出一种根据软件随机测试结果进行软件可靠性估计的方法,给出了软件可靠度的最优置信下限,并以此为基础对随机测试方案的优化设计进行了研究。     

软件可靠性综合模型的分析和研究

软件可靠性是系统可信性的重要属性之一.首先讨论了传统软件可靠性模型的主要弱点,接着提出了一种新的软件可靠性估算模型.当前,传统的软件可靠性度量模型中并未涉及大多数软件的复杂性及测试用例的有效性,从而使得在评估软件可靠性时不够精确,甚至是错误的.因此,提出了一些改进软件可靠性度量的方法.这些方法的主要思想是将影响软件复杂性的因素和测试用例的有效性并入软件可靠性模型中,以便使得该模型能更精确地反映实际情况.最后,大量的实验结果也表明了该模型的合理性及有用性.     

基于LASSO-LARS的软件复杂性度量属性特征选择研究

针对软件可靠性早期预测中软件复杂性度量属性维数灾难问题,提出了一种基于最小绝对值压缩与选择方法(The Least Absolute Shrinkage and Select Operator,LASSO)和最小角回归(Least Angle Regression,LARS)算法的软件复杂性度量属性特征选择方法。该方法筛选掉一些对早期预测结果影响较小的软件复杂性度量属性,得到与早期预测关系最为密切的关键属性子集。首先分析了LASSO回归方法的特点及其在特征选择中的应用,然后对LARS算法进行了修正,使其可以解决LASSO方法所涉及的问题,得到相关的复杂性度量属性子集。最后结合学习向量量化(Learning Vector Quantization,LVQ)神经网络进行软件可靠性早期预测,并基于十折交叉方法进行实验。通过与传统特征选择方法相比较,证明所提方法可以显著提高软件可靠性早期预测精度。     

软件可靠性及其评估

介绍了软件可靠性问题的来源,阐述了软件可靠性的定义、软件的失效机理、及软件可靠性与硬件可靠性间的差异,并提出了基于模糊数算术运算的软件可靠性估计方法。     

超几何分布模型与软件可靠性

本文提出了用超几何分布进行软件可靠性试验时,根据试验数据计算软件可靠度、失效率及MTBF的方法,从而拓宽了模型的应用范围.     

基于BP神经网络的软件可靠性模型选择

软件可靠性模型是软件可靠性工程研究的一个重要方面.如何在缺乏可靠性数据的情况下,选择合适的软件可靠性模型是对软件可靠性进行量化分析的关键.参照软件可靠性模型评价准则,根据聚类思想,对失效数据编码,采用反向传播神经网络进行聚类计算,从而实现了软件可靠性模型的选择.最后通过仿真实验证明了该方法的正确性和有效性.     

用例驱动的软件复杂性度量及应用

在用例驱动的软件开发过程中,研究软件复杂性,对于项目规模估算、进度控制及度量、评估都具有积极的意义.从需求获取、分析、设计、测试等阶段详细讨论了整个软件开发过程中如何获取、量化用例层次上的软件复杂度的方法,概括了研究的意义并通过实践进一步验证.根据它对软件可靠性相关指标参数的影响,探讨基于用例的软件可靠性度量分析方法的改进.     

基于使用模型的软件可靠性加速测试

给出了一种使用分层抽样和重要抽样方法并基于Markov链使用模型的软件可靠性加速测试的方法,在根据统计测试结果得到软件可靠性无偏估计的前提下,可以有效提高安全攸关软件的测试效率,部分解决了安全攸关软件统计测试时间和费用开销过大的问题.实例仿真结果表明,该方法可以有效地提高软件可靠性关键操作的测试效率.     

A Formal Interpretation of Software Testing as Inductive Inference

Software testing can be viewed as an inductive inference process during which the tester attempts to deduce software properties from its behaviour on a finite number of test cases. This paper investigates the foundation of software testing by interpreting the axioms of test adequacy criteria as properties of inductive inference. The interpretation manifests the conservative and simplest hypothesis nature of the induction underlying software testing. It also yields results relating adequate testing to software correctness and reliability. The convergence of the inductive inference process is proved to be a condition of the correctness of tested software. By measuring the convergence according to the probability of an inference result being correct up to a given error rate, a new approach to software reliability estimation is proposed, which differs from existing ones in taking software complexity into account.     

An experimental study of adaptive testing for software reliability assessment

Adaptive testing is a new form of software testing that is based on the feedback and adaptive control principle and can be treated as the software testing counterpart of adaptive control. Our previous work has shown that adaptive testing can be formulated and guided in theory to minimize the variance of an unbiased software reliability estimator and to achieve optimal software reliability assessment. In this paper, we present an experimental study of adaptive testing for software reliability assessment, where the adaptive testing strategy, the random testing strategy and the operational profile based testing strategy were applied to the Space program in four experiments. The experimental results demonstrate that the adaptive testing strategy can really work in practice and may noticeably outperform the other two. Therefore, the adaptive testing strategy can serve as a preferable alternative to the random testing strategy and the operational profile based testing strategy if high confidence in the reliability estimates is required or the real-world operational profile of the software under test cannot be accurately identified.     

Integrating testing with reliability

The activities of software testing and reliability are integrated for the purpose of demonstrating how the two activities interact in achieving testing efficiency and the reliability resulting from these tests. Integrating means modeling the execution of a variety of tests on a directed graph representation of an example program. A complexity metric is used to construct the nodes, edges, and paths of the example program. Models are developed to represent the efficiency and achieved reliability of black box and white box tests. Evaluations are made of path, independent path, node, program construct, and random tests to ascertain which, if any, is superior with respect to efficiency and reliability. Overall, path testing has the edge in test efficiency. The results depend on the nature of the directed graph in relation to the type of test. Although there is no dominant method, in most cases the tests that provide detailed coverage are better. For example, path testing discovers more faults than independent path testing. Predictions are made of the reliability and fault correction that results from implementing various test strategies. It is believed that these methods can be used by researchers and practitioners to evaluate the efficiency and reliability of other programs. Copyright © 2008 John Wiley & Sons, Ltd.     

一种模块化的实时软件可靠性测评方法

根据实时软件的可靠性评估需要,在分析传统软件可靠性增长模型局限性的基础上,提出了一种模块化的实时软件可靠性测评方法。该方法将系统的可靠性指标分配到各任务模块,并将贝叶斯理论用于单个模块的可靠性测评。相比基于可靠性增长模型的传统测试方法,该方法可降低系统测试的复杂程度,提高评估精度,特别适合对高可靠性软件的测评。     

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.     

软件可靠性度量方法

分析软件故障暴露率与软件测试次数之间的关系,提出在保证可靠性测试结果客观准确的前提下,有效减少验证测试次数的方法。结合软件可靠性和体系结构相关理论,提出基于组件的软件失效率定量计算方法。研究并利用软件可靠性度量方法,提高了软件可靠性测试效率和可靠性评估准确性。     

软件可靠性分析、测试与评估工具--SRATE介绍

软件可靠性分析、测试及评估工具(SRATE)是一个集软件可靠性分析、测试及评估于一体的软件工具。该工具支持多种测试策略下的软件自动化测试及评估试验,包括随机测试策略、自适应测试策略等。该工具还集成了包括欧洲航天局的space软件、浙大CAD&CG国家重点实验室的Intra3D构件库在内的多个典型被测对象。根据工具接口要求可以方便的添加新的测试策略和测试对象。具有自动化程度高、通用性强、配置灵活、易于扩展的特点,是进行软件可靠性分析研究工作的有力工具。     

基于重要抽样的软件统计测试加速

本文提出一种基于重要抽样的软件统计测试加速方法,该方法通过调整软件Markov链使用模型的迁移概率,在根据统计测试结果得到软件可靠性无偏估计的前提下,可以有效提高安全攸关软件的测试效率,部分解决了安全攸关软件统计测试时间和费用开销过大的问题。同时,本文给出了计算优化迁移概率的模拟退火算法。实验仿真结果表明,该方法可以有效地提高安全攸关软件统计测试的效率。     

基于经验数据的软件可靠性模型研究

软件的可靠性同其正确性和性能同样的重要,软件可靠性模型是软件可靠性定量分析的技术基础.对基于经验数据的软件可靠性模型进行了研究,并进行工程化实现,已应用于多个测试项目的测试评估,对软件的可靠性和测试的充分性进行了定量分析.