Relevant bibliographies by topics / TESTING DATA

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'TESTING DATA'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "TESTING DATA"

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Arjun, Mantri. "Enhancing Data Quality in Data Engineering using Data Testing Framework: Types and Tradeoffs." European Journal of Advances in Engineering and Technology 7, no. 10 (2020): 95–100. https://doi.org/10.5281/zenodo.13354036.

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Ensuring high data quality is critical in the era of big data, where reliable data is essential for accurate decision-making and business intelligence. This paper reviews various data testing frameworks designed to enhance data quality, including data validation, data cleansing, data profiling, data lineage, and automated testing frameworks. Each type of framework offers unique functionalities and presents distinct tradeoffs, such as customization versus complexity and real-time versus batch processing. By understanding these frameworks and their tradeoffs, data engineers can make informed dec
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Preyaa, Atri. "Enhancing the reliability and accuracy of data pipelines through effective testing and validation strategies: A comprehensive approach." European Journal of Advances in Engineering and Technology 10, no. 9 (2023): 52–56. https://doi.org/10.5281/zenodo.11213814.

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Today, modern data-driven applications are implemented as large-scale systems that use sophisticated data pipelines for ingestion, processing, and transformation of data. The main aspect of data integrity and reliable analytics is the control over how reliable and accurate these pipelines are. This paper describes several approaches to testing aimed at ensuring the high quality of data pipelines. We study a range of testing methodologies such as unit testing, integration testing, and end-to-end testing, each being applied in their specific context when developing a data pipeline. Additionally,
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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 (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 (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 stud
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Uzych, Leo. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (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 (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 (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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Girgis, Moheb. "Automatic Test Data Generation for Data Flow Testing Using a Genetic Algorithm." JUCS - Journal of Universal Computer Science 11, no. (6) (2005): 898–915. https://doi.org/10.3217/jucs-011-06-0898.

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One of the major difficulties in software testing is the automatic generation of test data that satisfy a given adequacy criterion. This paper presents an automatic test data generation technique that uses a genetic algorithm (GA), which is guided by the data flow dependencies in the program, to search for test data to cover its def-use associations. The GA conducts its search by constructing new test data from previously generated test data that are evaluated as effective test data. The approach can be used in test data generation for programs with/without loops and procedures. The proposed G
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Harrold, Mary Jean, and Mary Lou Soffa. "Interprocedual data flow testing." ACM SIGSOFT Software Engineering Notes 14, no. 8 (1989): 158–67. http://dx.doi.org/10.1145/75309.75327.

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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,
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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 cur
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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 censor
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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.<br>Includes bibliographical references (p. 119-123).<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>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 measure
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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<br>Ph.D.<br>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 pr
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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
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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 char
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Books on the topic "TESTING DATA"

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

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

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

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Held, Gilbert. Data communications testing and troubleshooting. H.W. Sams, 1988.

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

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

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Frieden, B. Roy. Probability, Statistical Optics, and Data Testing. 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. 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. AGARD, 1995.

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Advisory Group for Aerospace Research and Development. Structures and Materials Panel., ed. Advanced aeroservoelastic testing and data analysis. 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. 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. 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. 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. 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. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1446-5_8.

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Budworth, Paul, and Abdullah Tanira. "Flow Data." In Surface Well Testing. CRC Press, 2024. http://dx.doi.org/10.1201/9781032623689-11.

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Budworth, Paul, and Abdullah Tanira. "Data Instrumentation." In Surface Well Testing. CRC Press, 2024. http://dx.doi.org/10.1201/9781032623689-12.

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Schrader, Steffen Haakon. "Flight Test Data Comparison." In Flight Testing. 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. 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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55862-2_33.

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Conference papers on the topic "TESTING DATA"

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wang, xining, xiaojian hao, pan pei, et al. "Research on a method for repairing breakpoints in temperature data of colorimetric thermometers." In Optoelectronics Testing and Measurement, edited by Sen Han. SPIE, 2024. https://doi.org/10.1117/12.3046870.

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Romero-Maestre, Amor, Pedro Martín-Holgado, José de-Martín-Hernández, et al. "Upgrading Optocoupler Experimental Data on DD Neutron Radiation Testing." In 2024 RADECS Data Workshop. IEEE, 2024. https://doi.org/10.1109/radecs61975.2024.11017564.

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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. ACM Press, 2013. http://dx.doi.org/10.1145/2457317.2457319.

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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. American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-2856.

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Vivanti, Mattia. "Dynamic data-flow testing." In ICSE '14: 36th International Conference on Software Engineering. 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. 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. 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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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), 2001. http://dx.doi.org/10.2172/787923.

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Ramsey, Lori J., and Karen L. Fertig. Officer Standardized Educational Testing Data. Defense Technical Information Center, 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. Defense Technical Information Center, 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), 1996. http://dx.doi.org/10.2172/768865.

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

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Weibel, Marjorie. Data communication performance testing of ACTS. Institute for Telecommunication Sciences, 1995. https://doi.org/10.70220/6lwvb1pb.

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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, 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. National Bureau of Economic Research, 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), 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), 2015. http://dx.doi.org/10.2172/1172830.

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