Relevant bibliographies by topics / TESTING DATA

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'TESTING DATA'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "TESTING DATA"

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Kumar, Gagan, and Vinay Chopra. "Automatic Test Data Generation for Basis Path Testing." Indian Journal Of Science And Technology 15, no. 41 (November 5, 2022): 2151–61. http://dx.doi.org/10.17485/ijst/v15i41.1503.

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Golfarelli, Matteo, and Stefano Rizzi. "Data Warehouse Testing." International Journal of Data Warehousing and Mining 7, no. 2 (April 2011): 26–43. http://dx.doi.org/10.4018/jdwm.2011040102.

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Testing is an essential part of the design life-cycle of a software product. Although most phases of data warehouse design have received considerable attention in the literature, not much research has been conducted concerning data warehouse testing. In this paper, the authors introduce a number of data mart-specific testing activities, classify them in terms of what is tested and how it is tested, and show how they can be framed within a reference design method to devise a comprehensive and scalable approach. Finally, the authors discuss some practical evidences emerging from a real case study.
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Uzych, Leo. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (January 1996): 13–14. http://dx.doi.org/10.1097/00043764-199601000-00001.

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Rischitelli, Gary. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (January 1996): 14. http://dx.doi.org/10.1097/00043764-199601000-00002.

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Walczak, Przemysław, and Jadwiga Daszyńska-Daszkiewicz. "Testing microphysics data." Proceedings of the International Astronomical Union 9, S301 (August 2013): 221–28. http://dx.doi.org/10.1017/s1743921313014361.

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AbstractHigh precision asteroseismic data provide a unique opportunity to test input microphysics such as stellar opacities, chemical composition or equation of state. These tests are possible because pulsational frequencies as well as amplitudes and phases of the light variations are very sensitive to the internal structure of a star. We can therefore compute pulsation models and compare them with observations. The agreement or differences should tell us whether some models are adequate or not, and which input data need to be improved.
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Surname, Bob. "Testing CrossMark data." Journal of CrossMark Testing 1, no. 2 (2011): 20. http://dx.doi.org/10.5555/cm_test_2.

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Harrold, Mary Jean, and Mary Lou Soffa. "Interprocedual data flow testing." ACM SIGSOFT Software Engineering Notes 14, no. 8 (December 1989): 158–67. http://dx.doi.org/10.1145/75309.75327.

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Krueger, Alan B. "Stress Testing Economic Data." Business Economics 45, no. 2 (April 2010): 110–15. http://dx.doi.org/10.1057/be.2010.4.

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Faitelson, David, and Shmuel Tyszberowicz. "Data refinement based testing." International Journal of System Assurance Engineering and Management 2, no. 2 (June 2011): 144–54. http://dx.doi.org/10.1007/s13198-011-0060-y.

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Ray, L. Bryan. "Testing biochemical data by simulation." Science 369, no. 6502 (July 23, 2020): 387.10–389. http://dx.doi.org/10.1126/science.369.6502.387-j.

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Dissertations / Theses on the topic "TESTING DATA"

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Araujo, Roberto Paulo Andrioli de. "Scalable data-flow testing." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-14112014-155259/.

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Data-flow (DF) testing was introduced more than thirty years ago aiming at verifying a program by extensively exploring its structure. It requires tests that traverse paths in which the assignment of a value to a variable (a definition) and its subsequent reference (a use) is verified. This relationship is called definition-use association (dua). While control-flow (CF) testing tools have being able to tackle systems composed of large and long running programs, DF testing tools have failed to do so. This situation is in part due to the costs associated with tracking duas at run-time. Recently, an algorithm, called Bitwise Algorithm (BA), which uses bit vectors and bitwise operations for tracking intra-procedural duas at run-time, was proposed. This research presents the implementation of BA for programs compiled into Java bytecodes. Previous DF approaches were able to deal with small to medium size programs with high penalties in terms of execution and memory. Our experimental results show that by using BA we are able to tackle large systems with more than 250 KLOCs and 300K required duas. Furthermore, for several programs the execution penalty was comparable with that imposed by a popular CF testing tool.
Teste de fluxo de dados (TFD) foi introduzido há mais de trinta anos com o objetivo de criar uma avaliação mais abrangente da estrutura dos programas. TFD exige testes que percorrem caminhos nos quais a atribuição de valor a uma variável (definição) e a subsequente referência a esse valor (uso) são verificados. Essa relação é denominada associação definição-uso. Enquanto as ferramentas de teste de fluxo de controle são capazes de lidar com sistemas compostos de programas grandes e que executam durante bastante tempo, as ferramentas de TFD não têm obtido o mesmo sucesso. Esta situação é, em parte, devida aos custos associados ao rastreamento de associações definição-uso em tempo de execução. Recentemente, foi proposto um algoritmo --- chamado \\textit (BA) --- que usa vetores de bits e operações bit a bit para monitorar associações definição-uso em tempo de execução. Esta pesquisa apresenta a implementação de BA para programas compilados em Java. Abordagens anteriores são capazes de lidar com programas pequenos e de médio porte com altas penalidades em termos de execução e memória. Os resultados experimentais mostram que, usando BA, é possível utilizar TFD para verificar sistemas com mais de 250 mil linhas de código e 300 mil associações definição-uso. Além disso, para vários programas, a penalidade de execução imposta por BA é comparável àquela imposta por uma popular ferramenta de teste de fluxo de controle.
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McGaughey, Karen J. "Variance testing with data depth /." Search for this dissertation online, 2003. http://wwwlib.umi.com/cr/ksu/main.

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Khan, M. Shahan Ali, and Ahmad ElMadi. "Data Warehouse Testing : An Exploratory Study." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4767.

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Context. The use of data warehouses, a specialized class of information systems, by organizations all over the globe, has recently experienced dramatic increase. A Data Warehouse (DW) serves organiza-tions for various important purposes such as reporting uses, strategic decision making purposes, etc. Maintaining the quality of such systems is a difficult task as DWs are much more complex than ordi-nary operational software applications. Therefore, conventional methods of software testing cannot be applied on DW systems. Objectives. The objectives of this thesis study was to investigate the current state of the art in DW testing, to explore various DW testing tools and techniques and the challenges in DW testing and, to identify the improvement opportunities for DW testing process. Methods. This study consists of an exploratory and a confirmatory part. In the exploratory part, a Systematic Literature Review (SLR) followed by Snowball Sampling Technique (SST), a case study at a Swedish government organization and interviews were conducted. For the SLR, a number of article sources were used, including Compendex, Inspec, IEEE Explore, ACM Digital Library, Springer Link, Science Direct, Scopus etc. References in selected studies and citation databases were used for performing backward and forward SST, respectively. 44 primary studies were identified as a result of the SLR and SST. For the case study, interviews with 6 practitioners were conducted. Case study was followed by conducting 9 additional interviews, with practitioners from different organizations in Sweden and from other countries. Exploratory phase was followed by confirmatory phase, where the challenges, identified during the exploratory phase, were validated by conducting 3 more interviews with industry practitioners. Results. In this study we identified various challenges that are faced by the industry practitioners as well as various tools and testing techniques that are used for testing the DW systems. 47 challenges were found and a number of testing tools and techniques were found in the study. Classification of challenges was performed and improvement suggestions were made to address these challenges in order to reduce their impact. Only 8 of the challenges were found to be common for the industry and the literature studies. Conclusions. Most of the identified challenges were related to test data creation and to the need for tools for various purposes of DW testing. The rising trend of DW systems requires a standardized testing approach and tools that can help to save time by automating the testing process. While tools for operational software testing are available commercially as well as from the open source community, there is a lack of such tools for DW testing. It was also found that a number of challenges are also related to the management activities, such as lack of communication and challenges in DW testing budget estimation etc. We also identified a need for a comprehensive framework for testing data warehouse systems and tools that can help to automate the testing tasks. Moreover, it was found that the impact of management factors on the quality of DW systems should be measured.
Shahan (+46 736 46 81 54), Ahmad (+46 727 72 72 11)
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Andersson, Johan, and Mats Burberg. "Testing For Normality of Censored Data." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-253889.

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In order to make statistical inference, that is drawing conclusions from a sample to describe a population, it is crucial to know the correct distribution of the data. This paper focused on censored data from the normal distribution. The purpose of this paper was to answer whether we can test if data comes from a censored normal distribution. This by using normality tests and tests designed for censored data and investigate if we got correct size of these tests. This has been carried out with simulations in the program R for left censored data. The results indicated that with increasing censoring normality tests failed to accept normality in a sample. On the other hand the censoring tests met the requirements with increasing censoring level, which was the most important conclusion in this paper.
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Sestok, Charles K. (Charles Kasimer). "Data selection in binary hypothesis testing." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/16613.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.
Includes bibliographical references (p. 119-123).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Traditionally, statistical signal processing algorithms are developed from probabilistic models for data. The design of the algorithms and their ultimate performance depend upon these assumed models. In certain situations, collecting or processing all available measurements may be inefficient or prohibitively costly. A potential technique to cope with such situations is data selection, where a subset of the measurements that can be collected and processed in a cost-effective manner is used as input to the signal processing algorithm. Careful evaluation of the selection procedure is important, since the probabilistic description of distinct data subsets can vary significantly. An algorithm designed for the probabilistic description of a poorly chosen data subset can lose much of the potential performance available to a well-chosen subset. This thesis considers algorithms for data selection combined with binary hypothesis testing. We develop models for data selection in several cases, considering both random and deterministic approaches. Our considerations are divided into two classes depending upon the amount of information available about the competing hypotheses. In the first class, the target signal is precisely known, and data selection is done deterministically. In the second class, the target signal belongs to a large class of random signals, selection is performed randomly, and semi-parametric detectors are developed.
by Charles K. Sestok, IV.
Ph.D.
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Li, Yan. "Multiple Testing in Discrete Data Setting." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276747166.

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Clements, Nicolle. "Multiple Testing in Grouped Dependent Data." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/253695.

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Statistics
Ph.D.
This dissertation is focused on multiple testing procedures to be used in data that are naturally grouped or possess a spatial structure. We propose `Two-Stage' procedure to control the False Discovery Rate (FDR) in situations where one-sided hypothesis testing is appropriate, such as astronomical source detection. Similarly, we propose a `Three-Stage' procedure to control the mixed directional False Discovery Rate (mdFDR) in situations where two-sided hypothesis testing is appropriate, such as vegetation monitoring in remote sensing NDVI data. The Two and Three-Stage procedures have provable FDR/mdFDR control under certain dependence situations. We also present the Adaptive versions which are examined under simulation studies. The `Stages' refer to testing hypotheses both group-wise and individually, which is motivated by the belief that the dependencies among the p-values associated with the spatially oriented hypotheses occur more locally than globally. Thus, these `Staged' procedures test hypotheses in groups that incorporate the local, unknown dependencies of neighboring p-values. If a group is found significant, further investigation is done to the individual p-values within that group. For the vegetation monitoring data, we extend the investigation by providing some spatio-temporal models and forecasts to some regions where significant change was detected through the multiple testing procedure.
Temple University--Theses
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Chandorkar, Chaitrali Santosh. "Data Driven Feed Forward Adaptive Testing." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/1049.

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Test cost is a critical component in the overall cost of the product. Test cost varies in direct proportion with test time. This thesis introduces a data driven feed forward adaptive technique for reducing test time at wafer sort while maintaining the product defect level. Test data from first insertion of wafer is statistically analyzed to make a decision about adaptive test flow at subsequent insertions. The data driven feed forward technique uses a statistical screen to analyze test data from first probe of wafer and provides recommendations for test elimination at second insertions. At the second insertion dies are subjected to only the optimum number of tests for a reduced test flow. This technique is applicable only for the products which are tested at two or more insertions. The statistical screen identifies the dies for reduced test flow based upon correlation of tests across insertions. The tests which are repeated at both the insertions and are highly correlated are the candidates of elimination at second insertion. The feed forward technique is applied to a mixed signal analog product and figures of merit are evaluated. Application of technique to the production data shows that there is an average 55% test time reduction when a single site is tested per touchdown and up to 10% when 16 sites are tested in parallel per touchdown.
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林旭明 and Yuk-ming Lam. "Automation in soil testing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209774.

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Hu, Zongliang. "New developments in multiple testing and multivariate testing for high-dimensional data." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/534.

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This thesis aims to develop some new and novel methods in advancing multivariate testing and multiple testing for high-dimensional small sample size data. In Chapter 2, we propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling's tests, our proposed test statistics display some interesting characteristics. In particular, they are a summation of the log-transformed squared t-statistics rather than a direct summation of those components. More importantly, to derive the asymptotic normality of our test statistics under the null and local alternative hypotheses, we do not need the requirement that the covariance matrices follow a diagonal matrix structure. As a consequence, our proposed test methods are very flexible and readily applicable in practice. Monte Carlo simulations and a real data analysis are also carried out to demonstrate the advantages of the proposed methods. In Chapter 3, we propose a pairwise Hotelling's method for testing high-dimensional mean vectors. The new test statistics make a compromise on whether using all the correlations or completely abandoning them. To achieve the goal, we perform a screening procedure, pick up the paired covariates with strong correlations, and construct a classical Hotelling's statistic for each pair. While for the individual covariates without strong correlations with others, we apply squared t statistics to account for their respective contributions to the multivariate testing problem. As a consequence, our proposed test statistics involve a combination of the collected pairwise Hotelling's test statistics and squared t statistics. The asymptotic normality of our test statistics under the null and local alternative hypotheses are also derived under some regularity conditions. Numerical studies and two real data examples demonstrate the efficacy of our pairwise Hotelling's test. In Chapter 4, we propose a regularized t distribution and also explore its applications in multiple testing. The motivation of this topic dates back to microarray studies, where the expression levels of thousands of genes are measured simultaneously by the microarray technology. To identify genes that are differentially expressed between two or more groups, one needs to conduct hypothesis test for each gene. However, as microarray experiments are often with a small number of replicates, Student's t-tests using the sample means and standard deviations may suffer a low power for detecting differentially expressed genes. To overcome this problem, we first propose a regularized t distribution and derive its statistical properties including the probability density function and the moments. The noncentral regularized t distribution is also introduced for the power analysis. To demonstrate the usefulness of the proposed test, we apply the regularized t distribution to the gene expression detection problem. Simulation studies and two real data examples show that the regularized t-test outperforms the existing tests including Student's t-test and the Bayesian t-test in a wide range of settings, in particular when the sample size is small.
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Books on the topic "TESTING DATA"

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Held, Gilbert. Data communications: Testing and troubleshooting. Indianapolis: Howard W. Sams, 1989.

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Library of Congress. Congressional Research Service and United States. Federal Bureau of Investitgation, eds. DNA testing and data banking. Hauppauge, N.Y: Nova Science Publishers, 2011.

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Held, Gilbert. Data communications testing and troubleshooting. Indianapolis, Ind., USA: H.W. Sams, 1988.

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Held, Gilbert. Data communications testing and troubleshooting. 2nd ed. New York: Van Nostrand Reinhold, 1992.

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Saeed, Athar. Accelerated pavement testing: Data guidelines. Washington, D.C: Transportation Research Board, National Research Council, 2003.

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S, Page Gregory, Welge H. Robert, and Ames Research Center, eds. Propfan experimental data analysis. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.

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Frieden, B. Roy. Probability, Statistical Optics, and Data Testing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97289-8.

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Frieden, B. Roy. Probability, Statistical Optics, and Data Testing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56699-8.

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Development, North Atlantic Treaty Organization Advisory Group for Aerospace Research and. Advanced aeroservoelastic testing and data analysis. Neuilly-sur-Seine, France: AGARD, 1995.

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Advisory Group for Aerospace Research and Development. Structures and Materials Panel., ed. Advanced aeroservoelastic testing and data analysis. Neuilly sur Seine: Agard, 1995.

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Book chapters on the topic "TESTING DATA"

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Anderson, Alan J. B. "Testing hypotheses." In Interpreting Data, 65–84. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-3192-4_5.

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Curtis, Kevin, Lisa Dhar, Alan Hoskins, Mark Ayres, and Edeline Fotheringham. "Media Testing." In Holographic Data Storage, 151–83. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470666531.ch8.

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Hartigan, John A. "Testing for Antimodes." In Data Analysis, 169–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_14.

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Brandt, Siegmund. "Testing Statistical Hypotheses." In Data Analysis, 175–207. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03762-2_8.

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Brandt, Siegmund. "Testing Statistical Hypotheses." In Data Analysis, 212–47. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1446-5_8.

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Schrader, Steffen Haakon. "Flight Test Data Comparison." In Flight Testing, 159–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-63218-5_7.

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Srinivasan, John David. "Urine Testing." In Data Interpretation in Anesthesia, 175–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55862-2_32.

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de Armendi, Alberto J., and Gulshan Doulatram. "Drug Testing." In Data Interpretation in Anesthesia, 181–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55862-2_33.

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Kline, Kevin, Denis McDowell, Dustin Dorsey, and Matt Gordon. "Data Validation Testing." In Pro Database Migration to Azure, 285–94. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8230-4_11.

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Lebanon, Guy, and Mohamed El-Geish. "Essential Knowledge: Testing." In Computing with Data, 415–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98149-9_11.

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Conference papers on the topic "TESTING DATA"

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Cho, Jae-Han, and Lee-Sub Lee. "Testing Algebra for Data-driven Testing." In AST 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.45.17.

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ElGamal, Neveen, Ali ElBastawissy, and Galal Galal-Edeen. "Data warehouse testing." In the Joint EDBT/ICDT 2013 Workshops. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2457317.2457319.

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Vivanti, Mattia. "Dynamic data-flow testing." In ICSE '14: 36th International Conference on Software Engineering. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2591062.2591079.

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Dubey, Harsh Kumar, Prashant Kumar, Rahul Singh, Santosh K. Yadav, and Rama Shankar Yadav. "Automated data flow testing." In 2012 Students Conference on Engineering and Systems (SCES). IEEE, 2012. http://dx.doi.org/10.1109/sces.2012.6199072.

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Harrold, M., and M. Soffa. "Interprocedual data flow testing." In the ACM SIGSOFT '89 third symposium. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/75308.75327.

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Bruno, John L., Phillip B. Gibbons, and Steven Phillips. "Testing concurrent data structures." In the fifteenth annual ACM symposium. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/248052.248074.

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Punn, Narinder Singh, Sonali Agarwal, M. Syafrullah, and Krisna Adiyarta. "Testing Big Data Application." In 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2019. http://dx.doi.org/10.23919/eecsi48112.2019.8976972.

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McDonald, Darren, and Kyle Gardner. "Static VMCA Demonstrations: Safety and Data Implications." In 28th Aerodynamic Measurement Technology, Ground Testing, and Flight Testing Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-2856.

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Ai, Chiayu, and James C. Wyant. "Data Reduction for Phase Shift Interferometry." In Optical Fabrication and Testing. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oft.1986.tha11.

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Phase-shift interferometry (PSI) techniques have been widely used in modern phase-measurement instruments. The PSI technique is based on varying the phase difference between two beams in a known manner, and deriving the initial phase using three or more intensity measurements. The most common way to vary the phase difference between two beams is to apply a voltage to a piezoelectric (PZT) transducer upon which the reference mirror is mounted. The voltage applied is a ramp voltage or a step voltage with equal period and equal increment.
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"TESTING IN PARALLEL - A Need for Practical Regression Testing." In 5th International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003041503440348.

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Reports on the topic "TESTING DATA"

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McCrosson, F. J. ENDF/B Thermal Data Testing. Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/787923.

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Ramsey, Lori J., and Karen L. Fertig. Officer Standardized Educational Testing Data. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada259393.

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Clarke, Jerry, Kenneth Renard, and Brian Panneton. Data Analytics and Visualization for Large Army Testing Data. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada591620.

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K. M. Garcia. Supercritical Water Oxidation Data Acquisition Testing. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/768865.

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Chandorkar, Chaitrali. Data Driven Feed Forward Adaptive Testing. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1049.

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Wu, Jin Chu, and Raghu N. Kacker. Standard Errors and Significance Testing in Data Analysis for Testing Classifiers. National Institute of Standards and Technology, July 2021. http://dx.doi.org/10.6028/nist.ir.8383.

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Cawley, John, Euna Han, Jiyoon (June) Kim, and Edward Norton. Testing for Peer Effects Using Genetic Data. Cambridge, MA: National Bureau of Economic Research, August 2017. http://dx.doi.org/10.3386/w23719.

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Kahler, Albert Comstock. Data Testing CIELO Evaluations with ICSBEP Benchmarks. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1241649.

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Kahler, Albert Comstock III. Criticality Data Testing with CIELO Candidate Evaluations. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1172830.

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P. Dixon. Seepage Calibration Model and Seepage Testing Data. Office of Scientific and Technical Information (OSTI), February 2004. http://dx.doi.org/10.2172/837560.

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