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Journal articles on the topic 'TESTING DATA'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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

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12

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

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13

Praveen, Kumar Koppanati. "Parallelizing Automated UI and API Tests for Java Applications." Journal of Scientific and Engineering Research 10, no. 3 (2023): 155–59. https://doi.org/10.5281/zenodo.13912400.

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The growth and complexity of modern Java applications require newer testing strategies to make it efficient and faster. Automated testing, both for User Interfaces (UI) and Application Programming Interfaces (APIs), is crucial in maintaining the reliability and robustness of these applications. However, traditional approaches to automated testing often involve sequential execution, which can lead to significant delays in feedback and increased time-to-market. This paper discusses the parallelization of automated UI and API tests for Java applications to improve performance and testing efficien

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14

Kathiriya, Satish, Suraj Kumar, and Mahidhar Mullapudi. "Data - Driven Design Optimization: A/B Testing in Large - Scale Applications." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 1969–72. http://dx.doi.org/10.21275/sr24212165200.

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15

Kumar, Praveen. "Balancing Data Privacy and Software Testing Efficacy in AML Financial Systems." International Journal of Science and Research (IJSR) 14, no. 5 (2025): 619–23. https://doi.org/10.21275/sr25505094550.

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16

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

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17

Tarry, Claudia. "Misleading data from animal testing." Nursing Standard 18, no. 18 (2004): 31. http://dx.doi.org/10.7748/ns.18.18.31.s47.

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18

Kashyap, Shubhankar, and Avantika Singh. "Testing Data Feminism in India." Scholars Journal of Arts, Humanities and Social Sciences 9, no. 10 (2021): 516–30. http://dx.doi.org/10.36347/sjahss.2021.v09i10.006.

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Catherine D’Ignazio and Lauren F.Klein in their book Data Feminism argue that data models reflect existing power structures and social hierarchies. We aim to test this hypothesis in India on Instagram. Instagram is one of the most accessible and politically engaging social media platforms in India which makes its data models appropriate subjects for our study. The research question of our study is “Do Instagram data models disproportionately prioritise accounts that publish majoritarian feminist content over intersectional feminist content in India?” The paper employs two methodological approa

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19

Filippov, B. I., and T. B. Trush. "Processing of dependability testing data." Dependability 17, no. 2 (2017): 24–30. http://dx.doi.org/10.21683/1729-2646-2017-17-2-24-30.

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20

G, Zolzaya, and Usukhbayar B. "Hypothesis testing of agricultural data." Mongolian Journal of Agricultural Sciences 25, no. 03 (2018): 132–37. http://dx.doi.org/10.5564/mjas.v25i03.1182.

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The sustainable development strategy of the crop to implement environmental sustainability to be economically efficient, directed to improve soil productivity of the field and to increase the amount of harvest and to fully supply needs of food production by using research analysis. There are many types of methods for hypothesis testing in mathematical and statistical analyses. From these methods, T-stat and F-stat are widely used. However, they are in sufficient to express the final results, therefore we suggest to use Wald test, Variables test and Reset test. In addition to the quality

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21

Alizadeh Noughabi, Hadi, and Naser Reza Arghami. "Testing exponentiality using transformed data." Journal of Statistical Computation and Simulation 81, no. 4 (2011): 511–16. http://dx.doi.org/10.1080/00949650903348171.

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22

Gupta, Sunil. "Clinical data acceptance testing procedure." Pharmaceutical Programming 1, no. 2 (2008): 97–107. http://dx.doi.org/10.1179/175709206x372272.

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23

Rubin, Allen. "Significance Testing with Population Data." Social Service Review 59, no. 3 (1985): 518–20. http://dx.doi.org/10.1086/644316.

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24

Jorgensen, Alan A. "Testing with hostile data streams." ACM SIGSOFT Software Engineering Notes 28, no. 2 (2003): 9. http://dx.doi.org/10.1145/638750.638781.

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25

Hesse-Biber, Sharlene, and Paul Dupuis. "Testing Hypotheses on Qualitative Data." Social Science Computer Review 18, no. 3 (2000): 320–28. http://dx.doi.org/10.1177/089443930001800307.

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26

Alizadeh Noughabi, Hadi, and Naser Reza Arghami. "Testing Normality Using Transformed Data." Communications in Statistics - Theory and Methods 42, no. 17 (2013): 3065–75. http://dx.doi.org/10.1080/03610926.2011.611604.

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27

Konoplev, A. V., A. A. Bulgakov, V. E. Popov, et al. "Model Testing Using Chernobyl Data." Health Physics 70, no. 1 (1996): 8–12. http://dx.doi.org/10.1097/00004032-199601000-00002.

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28

Kryshev, I. I., T. G. Sazykina, I. N. Ryabov, V. K. Chumak, and O. L. Zarubin. "Model Testing Using Chernobyl Data." Health Physics 70, no. 1 (1996): 13–17. http://dx.doi.org/10.1097/00004032-199601000-00003.

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29

Garger, E. K., F. O. Hoffman, and C. W. Miller. "Model Testing Using Chernobyl Data." Health Physics 70, no. 1 (1996): 18–24. http://dx.doi.org/10.1097/00004032-199601000-00004.

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30

Linnet, Kristian. "Testing Normality of Transformed Data." Applied Statistics 37, no. 2 (1988): 180. http://dx.doi.org/10.2307/2347337.

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31

Mansour, Nashat, and Miran Salame. "Data Generation for Path Testing." Software Quality Journal 12, no. 2 (2004): 121–36. http://dx.doi.org/10.1023/b:sqjo.0000024059.72478.4e.

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32

Varrella, Gary F. "Using Water-Testing Data Sets." Science Activities: Classroom Projects and Curriculum Ideas 31, no. 3 (1994): 9–14. http://dx.doi.org/10.1080/00368121.1994.10113142.

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33

Curto, A., J. Aumont, J. F. Macías-Pérez, et al. "Testing Gaussianity on Archeops data." Astronomy & Astrophysics 474, no. 1 (2007): 23–33. http://dx.doi.org/10.1051/0004-6361:20066881.

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34

Pfeifer, Johannes, Michael Wahl, and Hans Wojtkowiak. "Data exchange formats for testing." Microprocessing and Microprogramming 27, no. 1-5 (1989): 687–93. http://dx.doi.org/10.1016/0165-6074(89)90133-6.

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35

Kafala, S. I., T. D. MacMahon, and P. W. Gray. "Testing of data evaluation methods." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 339, no. 1-2 (1994): 151–57. http://dx.doi.org/10.1016/0168-9002(94)91796-5.

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36

Laski, Janusz. "Data flow testing in STAD." Journal of Systems and Software 12, no. 1 (1990): 3–14. http://dx.doi.org/10.1016/0164-1212(90)90062-q.

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37

Woronow, Alex, Karen M. Love, and John C. Butler. "Interpreting and testing compositional data." Mathematical Geology 21, no. 1 (1989): 61–63. http://dx.doi.org/10.1007/bf00897240.

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38

Bashkansky, Emil, and Vladimir Turetsky. "Proficiency testing: binary data analysis." Accreditation and Quality Assurance 21, no. 4 (2016): 265–70. http://dx.doi.org/10.1007/s00769-016-1208-x.

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39

Knottnerus, J. André, and Peter Tugwell. "Multiple testing in big data." Journal of Clinical Epidemiology 67, no. 8 (2014): 843–44. http://dx.doi.org/10.1016/j.jclinepi.2014.05.009.

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40

Zhang, Beige, Chun Li, Nazaraf Shah, Xiang Fei, Lihong Jiang, and Hongming Cai. "A testing data validity assessment method and testing data validation platform based on SOA." Service Oriented Computing and Applications 12, no. 3-4 (2018): 201–9. http://dx.doi.org/10.1007/s11761-018-0242-4.

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41

Akshata, Khasge, and G. S. Nagaraja. "Securing Logs of Functional Testing Infrastructure by Masking Technique." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 1 (2021): 229–32. https://doi.org/10.35940/ijrte.A5910.0510121.

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As organizations become increasingly reliant on technology and data, data protection is now a mission critical priority. Ensuring that data is secure and uncorrupted is essential for avoiding downtime, ensuring productivity, and improving performance. Data protection is keeping the data i.e. confidential data secure from being corrupted or being compromised. Securing the data from logs also is essential when talking about data protection. The testing infrastructure which outputs logs may contain IP address as sensitive data, and this may be misused by other employs of the organization. So, by

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42

Uppuluri, Vijitha. "Leveraging Population-Level COVID-19 Testing Data for Predictive Modeling During Variant Surges: A Case Study from National Pharmacy Testing Network." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 2154–63. https://doi.org/10.21275/sr22051212116.

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43

Castro, Laura M., and Thomas Arts. "Testing Data Consistency of Data-Intensive Applications Using QuickCheck." Electronic Notes in Theoretical Computer Science 271 (March 2011): 41–62. http://dx.doi.org/10.1016/j.entcs.2011.02.010.

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44

Wang, Li. "Data Mining Technology Integrates Analysis in Petrochemical Testing Data." Procedia Computer Science 261 (2025): 946–53. https://doi.org/10.1016/j.procs.2025.04.482.

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45

McIntyre, Lauren M., and B. S. Weir. "Hardy-Weinberg Testing for Continuous Data." Genetics 147, no. 4 (1997): 1965–75. http://dx.doi.org/10.1093/genetics/147.4.1965.

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Abstract Estimation of allelic and genotypic distributions for continuous data using kernel density estimation is discussed and illustrated for some variable number of tandem repeat data. These kernel density estimates provide a useful representation of data when only some of the many variants at a locus are present in a sample. Two Hardy-Weinberg test procedures are introduced for continuous data: a continuous chi-square test with test statistic TCCS and a test based on Hellinger's distance with test statistic TCCS. Simulations are used to compare the powers of these tests to each other and t

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46

Sun, Yanqing, and Jimin Lee. "Testing independent censoring for longitudinal data." Statistica Sinica 21, no. 3 (2011): 1315–39. http://dx.doi.org/10.5705/ss.2009.251.

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47

ORIMO, ATSUSHI. "Graphical output of health testing data." Japanese journal of MHTS 12, no. 4 (1985): 323–30. http://dx.doi.org/10.7143/jhep1985.12.323.

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48

Orimo, Atsushi, and Masao Wakabayashi. "Graphical output of health testing data." Medical Informatics 15, no. 2 (1990): 141–49. http://dx.doi.org/10.3109/14639239008997666.

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49

Gaulier, Hurlin, and Jean-Pierre. "Testing Convergence: A Panel Data Approach." Annales d'Économie et de Statistique, no. 55/56 (1999): 411. http://dx.doi.org/10.2307/20076205.

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50

Andreev, A. V. "Probability, statistical optics, and data testing." Uspekhi Fizicheskih Nauk 162, no. 1 (1992): 147. http://dx.doi.org/10.3367/ufnr.0162.199201g.0147.

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