共查询到18条相似文献,搜索用时 78 毫秒
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Gupta方法的改进 总被引:2,自引:0,他引:2
Gupta等提出一种线性化谓词函数的方法(简称Gupta方法),为指定程序路径自动生成测试数据。该文给出了一种模型语言,研究静态,动态数据依赖关系的性质以及Gupta方法中各概念的形式化定义,将Gupta等提出的谓词出推广为路径静态切片,证明了路径静态切片构造算法的正确性,对Gupta方法的改进,省略了构造谓词片和输入依赖集的过程,改进后的方法构造线性约束的效率更高,以改进后的方法为核心算法,开发了面向路径的测试数据自动生成的原型工具,并用实际的程序路径对该工具进行实际,结果表明改进后的方法是比较有效的。 相似文献
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将遗传算法应用于覆盖指定路径的测试数据生成已得到了广泛的研究.具体实现中,影响测试数据生成效率的因素很多,如先验知识,GA参数,路径复杂度等.本文在简要介绍应用GA生成测试数据的关键技术后,设计实验分析了影响测试数据生成的部分因素,并据此得出了一些结论. 相似文献
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本文在研究软件测试数据自动生成技术的基础上,为决解空间爆炸的问题提出了将一种新兴的智能算法—菌群算法应用到软件测试数据的自动生成当中。但为了能准确并快速的生成测试数据还对算法进行了改进,然后在用随机法随机生成的测试数据覆盖被测程序大部分路径的基础上,运用改进后的菌群算法对剩余指定路径进行覆盖从而达到路径全覆盖的效果,实现测试数据自动生成系统。最后用一个简单实验验证了该系统的有效性。 相似文献
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测试数据生成是软件测试的核心与关键,本文介绍了迭代松弛法以及对迭代松弛法进行改进,改进后的方法比原方法生成测试数据的能力更强,不仅能够用于白盒测试数据的自动生成,还能够用于黑盒测试数据的自动生成。在此基础上提出一个面向路径的测试数据生成框架。并讨论该框架在单元测试、组装测试中的应用。 相似文献
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遗传算法及其在软件测试数据生成中的应用研究 总被引:9,自引:1,他引:8
文中首先给出了遗传算法的形式化的表示和一个基于此算法的测试数据生成系统原型。然后结合一个典型的测试单元例程,介绍系统实现中必须解决的参数选取、参数编码、评价函数的构造及驱动模块自动生成等关键问题。最后,介绍了一个实用的Ada软件测试数据生成工具——TCAG。 相似文献
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软件测试数据自动生成是软件测试中的重要难题之一。测试数据自动生成问题可归结为测试数据的搜索或组合优化问题,通常具有不连续、不可微和非线性等特征,适合于采用遗传算法、神经网络等人工智能技术进行解决。国内外学者在此方面作了不少研究并取得一定的成果,但也存在一些问题。该文系统地综述了近年来软件测试数据智能化生成的研究和存在的问题,并对未来的发展进行了展望。 相似文献
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测试用例的自动生成是软件测试研究的主要方向之一。针对现有的UML模型驱动测试方法在测试数据生成方面存在低效、无目的性、冗余等问题,提出了基于UML活动图模型驱动的划分测试用例自动生成方法。该方法将测试场景归为五种类型,并为每种类型规划了测试数据取值范围和选择方法。构建了基于该方法的原型工具软件,提供被测软件原模型和数据约束即可生成测试用例。实验结果表明,该方法能够在不降低测试覆盖率的情况下,能生成数量少、针对性强的测试用例集。 相似文献
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Automatic test data generation usually concerns identifying input values that cause a selected path to execute. If a given path involves pointers, then input values may be represented in terms of two‐dimensional dynamic data structures such as lists or trees. Thus, it is very important to identify the shape of the input data structure describing how many nodes are required and how nodes are connected each other. The approach presented in this paper makes use of the points‐to information for each statement in the selected path for the shape generation. It also converts each statement into a static single assignment (SSA) form without pointer dereferences. The SSA form serves as a system of constraints to be solved to yield input values for non‐pointer types. An empirical evaluation shows that shape generation can be achieved in linear time in terms of the number of pointer dereference operations. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Test data generation is one of the most technically challenging steps of testing software, but most commercial systems currently incorporate very little automation for this step. This paper presents results from a project that is trying to find ways to incorporate test data generation into practical test processes. The results include a new procedure for automatically generating test data that incorporates ideas from symbolic evaluation, constraint‐based testing, and dynamic test data generation. It takes an initial set of values for each input, and dynamically ‘pushes’ the values through the control‐flow graph of the program, modifying the sets of values as branches in the program are taken. The result is usually a set of values for each input parameter that has the property that any choice from the sets will cause the path to be traversed. This procedure uses new analysis techniques, offers improvements over previous research results in constraint‐based testing, and combines several steps into one coherent process. The dynamic nature of this procedure yields several benefits. Moving through the control flow graph dynamically allows path constraints to be resolved immediately, which is more efficient both in space and time, and more often successful than constraint‐based testing. This new procedure also incorporates an intelligent search technique based on bisection. The dynamic nature of this procedure also allows certain improvements to be made in the handling of arrays, loops, and expressions; language features that are traditionally difficult to handle in test data generation systems. The paper presents the test data generation procedure, examples to explain the working of the procedure, and results from a proof‐of‐concept implementation. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献
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Existing automated test data generation techniques tend to start from scratch, implicitly assuming that no pre‐existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre‐existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search‐based test data regeneration, a technique that can generate additional test data from existing test data using a meta‐heuristic search algorithm. The proposed technique is compared to a widely studied test data generation approach in terms of both efficiency and effectiveness. The empirical evaluation shows that test data regeneration can be up to 2 orders of magnitude more efficient than existing test data generation techniques, while achieving comparable effectiveness in terms of structural coverage and mutation score. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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Previous research using genetic algorithms to automate the generation of data for path testing has utilized several different fitness functions, assessing their usefulness by comparing them to random generation. This paper describes two sets of experiments that assess the performance of several fitness functions, relative to one another and to random generation. The results demonstrate that some fitness functions provide better results than others, generating fewer test cases to exercise a given program path. In these studies, the branch predicate and inverse path probability approaches were the best performers, suggesting that a two‐step process combining these two methods may be the most efficient and effective approach to path testing. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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An integrated automatic test data generation system 总被引:3,自引:0,他引:3
A. Jefferson Offutt 《Journal of Systems Integration》1991,1(3-4):391-409
The Godzilla automatic test data generator is an integrated collection of tools that implements a relatively new test data generation method—constraint-based testing—that is based on mutation analysis. Constraint-based testing integrates mutation analysis with several other testing techniques, including statement coverage, branch coverage, domain perturbation, and symbolic evaluation. Because Godzilla uses a rule-based approach to generate test data, it is easily extendible to allow new testing techniques to be integrated into the current system. This article describes the system that has been built to implement constraint-based testing. Godzilla's design emphasizes orthogonality and modularity, allowing relatively easy extensions. Godzilla's internal structure and algorithms are described with emphasis on internal structures of the system and the engineering problems that were solved during the implementation.Parts of this research were supported by Contract F30602-85-C-0255 through Rome Air Development Center while the author was a graduate student at the Georgia Institute of Technology. 相似文献