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71.
Category Partition Method (CPM) is a general approach to specification-based program testing, where test frame reduction and
refinement are two important issues. Test frame reduction is necessary since too many test frames may be produced, and test
frame refinement is important since during CPM testing new information about test frame generation may be achieved and considered
incrementally. Besides the information provided by testers or users, implementation related knowledge offers alternative information
for reducing and refining CPM test frames. This paper explores the idea by proposing a call patterns semantics based test
frame updating method for Prolog programs, in which a call patterns analysis is used to collect information about the way
in which procedures are used in a program. The updated test frames will be represented as constraints. The effect of our test
frame updating is two-fold. On one hand, it removes “uncared” data from the original set of test frames; on the other hand,
it refines the test frames to which we should pay more attention. The first effect makes the input domain on which a procedure
must be tested a subset of the procedure’s input domain, and the latter makes testers stand more chance to find out the faults
that are more likely to show their presence in the use of the program under consideration. Our test frame updating method
preserves the effectiveness of CPM testing with respect to the detection of faults we care. The test case generation from
the updated set of test frames is also discussed. In order to show the applicability of our method an approximation call patterns
semantics is proposed, and the test frame updating on the semantics is illustrated by an example.
相似文献
Lingzhong ZhaoEmail: |
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74.
基于HLA/RTI和分布式数据库初步构建了一个开放的测试和评估应用框架,为战术C4KISR系统中的指挥、控制、通信(C3)以及典型飞行器武器系统的作战效能(K)提供一种有效的可扩展的测试评估环境.系统采用了基于HLA/RTI的分布式体系结构,与被测系统相连.建立了典型的层次化战术CAKISR系统效能指标体系,并实现相应的评估算法模型.基于Oracle数据库建立了通用的战术C4KISR系统试验数据库/用例库/算法库和相应的管理工具,并开发了通用的分析评估工具,支持评估指标的在线/离线计算和显示. 相似文献
75.
提出了一种基于粗糙集的不完备测试数据填补方法。该方法首先利用粗糙集中下近似集的性质对随机生成的测试数据进行填补,然后根据属性数据的取值概率函数求出的结果进行二次填补,从而完成对不完备测试数据的完备化处理,生成最优测试用例。采用本方法可以较好地反映待测系统所蕴含的规则,且可以避免测试数据的冲突。 相似文献
76.
在马尔可夫链模型的基础上,将测试问题转化为一个数学问题。通过建立软件的使用链,根据使用链进行序列抽样,产生测试用例,将软件测试结果的分析问题转化为一个经典概率问题。运用实例证明,这种技术具有一定的实用性和有效性。 相似文献
77.
本文简单介绍了软件测试,并对人们意识中一些比较普遍的关于软件测试的误区进行剖析,使人们对软件测试的认识更为清晰。 相似文献
78.
UML代表着软件建模的发展趋势,对基于UML模型的测试技术研究具有现实意义。本文采用基于UML模型的场景测试技术生成测试用例。 相似文献
79.
在社会经济体系研究中,从系统结构和仿真的技术人手,在综合利用SPSS等软件的基础上,通过矩阵运算,测算出行业收入差距基尼系数和泰尔指数,发现行业收入差距不断扩大.根据经济学和系统工程理论筛选影响行业收入差距的因素,构建ISM模型,对行业收入差距成因系统进行测试,通过T检验优化模型和ISM层次筛选法得出主要原因.综合分析得出结论,微观因素行业收入差距产生和扩大的主要原因,经济体制市场化改革足差距产生和扩大的重要环境因素. 相似文献
80.
Mutation testing has traditionally been used as a defect injection technique to assess the effectiveness of a test suite as
represented by a “mutation score.” Recently, mutation testing tools have become more efficient, and industrial usage of mutation
analysis is experiencing growth. Mutation analysis entails adding or modifying test cases until the test suite is sufficient
to detect as many mutants as possible and the mutation score is satisfactory. The augmented test suite resulting from mutation
analysis may reveal latent faults and provides a stronger test suite to detect future errors which might be injected. Software
engineers often look for guidance on how to augment their test suite using information provided by line and/or branch coverage
tools. As the use of mutation analysis grows, software engineers will want to know how the emerging technique compares with
and/or complements coverage analysis for guiding the augmentation of an automated test suite. Additionally, software engineers
can benefit from an enhanced understanding of efficient mutation analysis techniques. To address these needs for additional
information about mutation analysis, we conducted an empirical study of the use of mutation analysis on two open source projects.
Our results indicate that a focused effort on increasing mutation score leads to a corresponding increase in line and branch
coverage to the point that line coverage, branch coverage and mutation score reach a maximum but leave some types of code
structures uncovered. Mutation analysis guides the creation of additional “common programmer error” tests beyond those written
to increase line and branch coverage. We also found that 74% of our chosen set of mutation operators is useful, on average,
for producing new tests. The remaining 26% of mutation operators did not produce new test cases because their mutants were
immediately detected by the initial test suite, indirectly detected by test suites we added to detect other mutants, or were
not able to be detected by any test.
Ben Smith is a second year Ph.D. student in Computer Science at North Carolina State University working as an RA under Dr. Laurie Williams. He received his Bachelor’s degree in Computer Science in May of 2007 and he hopes to receive his doctorate in 2012. He has begun work on developing SQL Coverage Metrics as a predictive measure of the security of a web application. This fall, he will be beginning the doctoral preliminary exam and working as a Testing Manager for the NCSU CSC Senior Design Center: North Carolina State’s capstone course for Computer Science. Finally, he has designed and maintained the websites for the Center for Open Software Engineering and ESEM 2009. Laurie Williams is an Associate Professor in the Computer Science Department of the College of Engineering at North Carolina State University. She leads the Software Engineering Reasearch group and is also the Director of the North Carolina State University Laboratory for Collaborative System Development and the Center for Open Software Engineering. She is also technical co-director of the Center for Open Software Engineering (COSE) and the area technical director of the Secure Open Systems Initiative (SOSI) at North Carolina State University. Laurie received her Ph.D. in Computer Science from the University of Utah, her MBA from Duke University, and her BS in Industrial Engineering from Lehigh University. She worked for IBM for nine years in Raleigh, NC before returning to academia. Laurie’s research interests include agile software development methodologies and practices, collaborative/pair programming, software reliability and testing, and software engineering for secure systems development. 相似文献
Laurie WilliamsEmail: |
Ben Smith is a second year Ph.D. student in Computer Science at North Carolina State University working as an RA under Dr. Laurie Williams. He received his Bachelor’s degree in Computer Science in May of 2007 and he hopes to receive his doctorate in 2012. He has begun work on developing SQL Coverage Metrics as a predictive measure of the security of a web application. This fall, he will be beginning the doctoral preliminary exam and working as a Testing Manager for the NCSU CSC Senior Design Center: North Carolina State’s capstone course for Computer Science. Finally, he has designed and maintained the websites for the Center for Open Software Engineering and ESEM 2009. Laurie Williams is an Associate Professor in the Computer Science Department of the College of Engineering at North Carolina State University. She leads the Software Engineering Reasearch group and is also the Director of the North Carolina State University Laboratory for Collaborative System Development and the Center for Open Software Engineering. She is also technical co-director of the Center for Open Software Engineering (COSE) and the area technical director of the Secure Open Systems Initiative (SOSI) at North Carolina State University. Laurie received her Ph.D. in Computer Science from the University of Utah, her MBA from Duke University, and her BS in Industrial Engineering from Lehigh University. She worked for IBM for nine years in Raleigh, NC before returning to academia. Laurie’s research interests include agile software development methodologies and practices, collaborative/pair programming, software reliability and testing, and software engineering for secure systems development. 相似文献