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Journal articles on the topic 'Data-based testing'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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1

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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2

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 (September 27, 2018): 201–9. http://dx.doi.org/10.1007/s11761-018-0242-4.

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3

Forgács, István, and Antonia Bertolino. "Preventing untestedness in data-flow based testing." Software Testing, Verification and Reliability 12, no. 1 (November 19, 2001): 29–58. http://dx.doi.org/10.1002/stvr.234.

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4

Golfarelli, Matteo, and Stefano Rizzi. "Data warehouse testing: A prototype-based methodology." Information and Software Technology 53, no. 11 (November 2011): 1183–98. http://dx.doi.org/10.1016/j.infsof.2011.04.002.

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5

SUN, Pei-gang, Li-qun TAN, Li-jun ZHAO, and Jing-po WU. "Software testing method based on data processing model." Journal of Computer Applications 29, no. 10 (December 28, 2009): 2827–29. http://dx.doi.org/10.3724/sp.j.1087.2009.02827.

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6

Bhatti, Muhammad Ishaq. "Testing regression models based on sample survey data." Bulletin of the Australian Mathematical Society 48, no. 2 (October 1993): 351–52. http://dx.doi.org/10.1017/s000497270001577x.

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7

Prys-Williams, Allan G., and M. I. Bhatti. "Testing Regression Models Based on Sample Survey Data." Statistician 45, no. 4 (1996): 529. http://dx.doi.org/10.2307/2988553.

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8

Lin, A. N., and B. A. Verser. "Microcomputer Based Data Acquisition System for Dynamic Testing." Earthquake Spectra 3, no. 2 (May 1987): 299–313. http://dx.doi.org/10.1193/1.1585430.

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The availability of capable and relatively inexpensive microcomputers and versatile expansion devices has revolutionized education, research, and practice in engineering. A data acquisition and analysis system for use in full scale and large model dynamic testing based on a popular series of microcomputer has been developed. “Quick-look” and preliminary frequency analysis of data are completed in the microcomputer environment, while more detailed frequency domain analysis of single and dual channel data are completed in the mainframe environment. The system has been used for ambient and forced vibration testing of several prototype structures and large models.

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9

Greenblatt, Richard E., and Mark E. Pflieger. "Randomization-Based Hypothesis Testing from Event-Related Data." Brain Topography 16, no. 4 (2003): 225–32. http://dx.doi.org/10.1023/b:brat.0000032856.48286.18.

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10

Pakyari, Reza, and N. Balakrishnan. "Testing exponentiality based on Type-I censored data." Journal of Statistical Computation and Simulation 83, no. 12 (December 2013): 2369–78. http://dx.doi.org/10.1080/00949655.2012.691974.

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11

Landaburu, E., D. Morales, and L. Pardo. "Divergence-based estimation and testing with misclassified data." Statistical Papers 46, no. 3 (July 2005): 397–409. http://dx.doi.org/10.1007/bf02762841.

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12

Ge, Youngchao, Sandrine Dudoit, and Terence P. Speed. "Resampling-based multiple testing for microarray data analysis." Test 12, no. 1 (June 2003): 1–77. http://dx.doi.org/10.1007/bf02595811.

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13

Wang, Fang, Ri Na Wu, Shu Fang Lee, and Tian Hua Zheng. "An Approach to Mutation-Based Test Data Generation." Applied Mechanics and Materials 347-350 (August 2013): 491–95. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.491.

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Software testing is an important technology used to assure the quality of software. The design of test data is very important, which determines the testing effect in the software testing. Existing design approach to test data cant simulate the fault in software run-environment systematically. Mutation testing is an effective software testing method, which can simulate software defects systematically. Using mutation testing for reference, this paper proposes an approach to mutation-based test data generation. By analyzing the demands of test data, such as coverage rate, fault simulation, we design a series of data mutation operators, which can accomplish design of test data systematically. Experiments are carried out on the supporting tool to validate the effectiveness of this approach.

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14

Abbasnejad, M. "Testing Exponentiality Based on Renyi Entropy of Transformed Data." Journal of Statistical Research of Iran 8, no. 2 (March 1, 2012): 149–62. http://dx.doi.org/10.18869/acadpub.jsri.8.2.149.

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15

Marzec, Leszek, and Pawel Marzec. "Testing based on sampled data for proportional hazards model." Statistics & Probability Letters 37, no. 3 (March 1998): 303–13. http://dx.doi.org/10.1016/s0167-7152(97)84157-0.

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16

Varshney, Sapna, and Monica Mehrotra. "Search based software test data generation for structural testing." ACM SIGSOFT Software Engineering Notes 38, no. 4 (July 12, 2013): 1–6. http://dx.doi.org/10.1145/2492248.2492277.

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17

Yigit, Hulya D., Miguel A. Sorrel, and Jimmy de la Torre. "Computerized Adaptive Testing for Cognitively Based Multiple-Choice Data." Applied Psychological Measurement 43, no. 5 (September 18, 2018): 388–401. http://dx.doi.org/10.1177/0146621618798665.

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18

Waller, Matias. "Data-Based Testing for Nonlinearity in Dynamical Systems: The Use of Surrogate Data." IEEE Transactions on Control Systems Technology 27, no. 2 (March 2019): 679–88. http://dx.doi.org/10.1109/tcst.2017.2771431.

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19

Yang, Tie Zao, Xin Yang Wang, and Chang Sheng Wang. "Method of Data Processing Based on LaBVIEW." Advanced Materials Research 1078 (December 2014): 430–34. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.430.

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The data processing is the important link in the process of various engineering testing and experiment. The theory and method of the data processing are correct will directly affect the accuracy of the efficiency of testing and conclusions. This paper introduces the software written in LaBVIEW to read the data acquisition system. The data of the motorcycle ignition advance Angle and fuel injection advance Angle signal data were collected by MP424 then waveform synthesis, stratified by LaBVIEW processed directly calculate the eigenvalues of the test data, and directly displayed in the chart..It to process collected data provides a new method, this method simplifies the process of data analysis, improve the efficiency of test.

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20

Hakulinen, Timo, Leena Tenkanen, Kamal Abeywickrama, and Lassi Paivarinta. "Testing Equality of Relative Survival Patterns Based on Aggregated Data." Biometrics 43, no. 2 (June 1987): 313. http://dx.doi.org/10.2307/2531815.

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21

Mallick, Subhasis, and Dipankar Majumdar. "Genetic Algorithm Based Test Data Generation for Structured Software Testing." Journal of the Association of Engineers, India 85, no. 3-4 (December 1, 2015): 52. http://dx.doi.org/10.22485/jaei/2015/v85/i3-4/119867.

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22

Wu, Lang, and Michael D. Perlman. "Testing lattice conditional independence models based on monotone missing data." Statistics & Probability Letters 50, no. 2 (November 2000): 193–201. http://dx.doi.org/10.1016/s0167-7152(00)00098-5.

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23

Van Steen, K., N. M. Laird, P. Markel, and G. Molenberghs. "Approaches to Handling Incomplete Data in Family-based Association Testing." Annals of Human Genetics 71, no. 2 (March 2007): 141–51. http://dx.doi.org/10.1111/j.1469-1809.2006.00325.x.

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24

Cabilio, P., and J. Peng. "Multiple rank-based testing for ordered alternatives with incomplete data." Statistics & Probability Letters 78, no. 16 (November 2008): 2609–13. http://dx.doi.org/10.1016/j.spl.2008.08.001.

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25

Hummer, Waldemar, Orna Raz, Onn Shehory, Philipp Leitner, and Schahram Dustdar. "Testing of data-centric and event-based dynamic service compositions." Software Testing, Verification and Reliability 23, no. 6 (April 15, 2013): 465–97. http://dx.doi.org/10.1002/stvr.1493.

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26

BLOCK, GLADYS, ANNE M. HARTMAN, CONNIE M. DRESSER, MARGARET D. CARROLL, JANET GANNON, and LILLY GARDNER. "A DATA-BASED APPROACH TO DIET QUESTIONNAIRE DESIGN AND TESTING." American Journal of Epidemiology 124, no. 3 (September 1986): 453–69. http://dx.doi.org/10.1093/oxfordjournals.aje.a114416.

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27

Liu, Chien-Hung. "Data flow analysis and testing of JSP-based Web applications." Information and Software Technology 48, no. 12 (December 2006): 1137–47. http://dx.doi.org/10.1016/j.infsof.2006.06.003.

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28

Qi, Yu, David Kung, and Eric Wong. "An agent-based data-flow testing approach for Web applications." Information and Software Technology 48, no. 12 (December 2006): 1159–71. http://dx.doi.org/10.1016/j.infsof.2006.06.005.

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29

Jee, Eunkyoung, Junbeom Yoo, Sungdeok Cha, and Doohwan Bae. "A data flow-based structural testing technique for FBD programs." Information and Software Technology 51, no. 7 (July 2009): 1131–39. http://dx.doi.org/10.1016/j.infsof.2009.01.003.

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30

McAdam, Bruce J., Timothy B. Grabowski, and Guðrún Marteinsdóttir. "Testing for differences in spatial distributions from individual based data." Fisheries Research 127-128 (September 2012): 148–53. http://dx.doi.org/10.1016/j.fishres.2012.02.024.

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31

Laibinis, Linas, Alexei Iliasov, and Alexander Romanovsky. "Mutation Testing for Rule-Based Verification of Railway Signaling Data." IEEE Transactions on Reliability 70, no. 2 (June 2021): 676–91. http://dx.doi.org/10.1109/tr.2020.3047462.

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32

LIU, CHIEN-HUNG, DAVID C. KUNG, PEI HSIA, and CHIH-TUNG HSU. "AN OBJECT-BASED DATA FLOW TESTING APPROACH FOR WEB APPLICATIONS." International Journal of Software Engineering and Knowledge Engineering 11, no. 02 (April 2001): 157–79. http://dx.doi.org/10.1142/s0218194001000499.

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In recent years, Web applications have grown rapidly because of their abilities to provide online information access to anyone at anytime around the world. As Web applications become complex, there is a growing concern about their quality and reliability. This paper extends traditional data flow testing techniques to Web applications. Several data flow issues about analyzing HyperText Markup Language (HTML) documents in Web applications are discussed. An object-based data flow testing approach is presented. The approach is based on a test model that captures data flow test artifacts of Web applications. In the test model, each entity of a Web application is modeled as an object. The data flow information of the functions within an object or across objects is then captured using various flow graphs. Based on the object-based test model, data flow test cases for a Web application can be systematically and selectively generated in five different levels.

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33

Chen, Kai, Hong Tan, Jie Gao, and Yang Lu. "Big Data Based Design of Food Safety Cloud Platform." Applied Mechanics and Materials 536-537 (April 2014): 583–87. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.583.

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Food safety has been in the spotlight of the global attention. In 2013, Chinas food safety supervision policy was undertaken a major reform. China Food and Drug Administration (CFDA) takes over the responsibility of food safety management, which in the past was conducted by different government sectors. Under this new management system, how to carry out the practical work has become a new issue. This paper introduces an innovative food safety management mode adopted by Guizhou province. On the basis of latest information technology, food production enterprises, government, testing organizations and consumers are integrated into a unified food safety information service cloud platform. The core technology of cloud platform is composed of food safety knowledge system, testing management system, food safety information publicity system as well as mobile application. The food factory inspection data, government inspection data, testing organizations testing data and consumers purchasing information are integrated into food safety and nutrient test big data. Utilizing the data to explore the information that is needed by all the parties, this paper tries to provide a solution to the risk exchange problem faced by Chinas food safety issue. At the same time, food safety problem can be solved through the contribution of different stakeholders.

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34

Ma, Zhi Gang, and Wen Yi Liu. "Design of Data Acquisition System Based on PXI Bus and VC++2008." Advanced Materials Research 712-715 (June 2013): 2702–5. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2702.

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In order to satisfy the multi-channel space testing requirement, multi-channel (temperature, liquid level, voltage, etc) testing system is designed based on PXI Bus. VC++2008 is adopted for acquisition and control programming, and the testing data can be real-time displayed in the interface. This system has already been successfully implemented in a testing task.

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35

Liu, Di, and Shaoping Wang. "Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process." Reliability Engineering & System Safety 205 (January 2021): 107231. http://dx.doi.org/10.1016/j.ress.2020.107231.

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36

Tang, Ling, Chenghao Wang, and Shuai Wang. "Energy Time Series Data Analysis based on a Novel Integrated Data Characteristic Testing Approach." Procedia Computer Science 17 (2013): 759–69. http://dx.doi.org/10.1016/j.procs.2013.05.098.

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37

Zhou, Chuan Hong, Hai Shi, and Xiao Ning Wang. "Study on RoHS Data Integration System Based on Optical Character Recognition." Applied Mechanics and Materials 220-223 (November 2012): 2083–86. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2083.

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Nowadays, more and more companies are going to adapt themselves to restriction on the use of certain hazardous substances (RoHS) compliance. There are many RoHS testing equipments, so there are many RoHS Data formats. It is great inconvenient for the enterprise information management. This research puts forward to OCR technology as the foundation, using OPC technology and FTP technology is suitable for the construction of the RoHS testing data integration system framework which solves the RoHS testing field to the problem of data integration.

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38

Chen, Kai, Miao Hao, Hong Tan, Wei Huang, Fangjian Xin, and Bin Su. "The Construction of Food Testing Intelligent Laboratory Based on “Internet+” and Big Data Technology." E3S Web of Conferences 292 (2021): 02053. http://dx.doi.org/10.1051/e3sconf/202129202053.

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Food safety testing is of great significance for building a harmonious society. With the advent of the Internet era, the number of data indicators to be considered in food safety testing has gradually increased, and traditional food testing cannot adapt to the fast pace of today’s times. In order to meet the current challenges of food testing, this paper analyses the advantages of “Internet +” in building a food testing laboratory based on the challenges of food safety testing in the Internet era, and discusses the advantages of “Internet +” in the era of food testing. The paper discusses the intelligent working mode of food testing laboratories in the “Internet +” era, and combines big data technology to build an intelligent and comprehensive laboratory management platform, so as to build a new era of food testing intelligent laboratory, which improves the efficiency of food testing managers, reduces the workload, improves the accuracy of food safety testing, and helps the healthy development of the food industry.

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39

Yang, Rui, Zhenyu Chen, Zhiyi Zhang, and Baowen Xu. "EFSM-Based Test Case Generation: Sequence, Data, and Oracle." International Journal of Software Engineering and Knowledge Engineering 25, no. 04 (May 2015): 633–67. http://dx.doi.org/10.1142/s0218194015300018.

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Model-based testing has been intensively and extensively studied in the past decades. Extended Finite State Machine (EFSM) is a widely used model of software testing in both academy and industry. This paper provides a survey on EFSM-based test case generation techniques in the last two decades. All techniques in EFSM-based test case generation are mainly classified into three parts: test sequence generation, test data generation, and test oracle construction. The key challenges, such as coverage criterion and feasibility analysis in EFSM-based test case generation are discussed. Finally, we summarize the research work and present several possible research areas in the future.

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40

Jaffari, Aman, and Cheol-Jung Yoo. "An Experimental Investigation into Data Flow Annotated-Activity Diagram-Based Testing." Journal of Computing Science and Engineering 13, no. 3 (September 30, 2019): 107–23. http://dx.doi.org/10.5626/jcse.2019.13.3.107.

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41

Feng, Yong Qin, Yan Yin, Xiao Rui Zhang, and Jin jian Xiao. "Testing and Evaluation of Baby Walkers Based on Product Injury Data." Advanced Materials Research 834-836 (October 2013): 1628–31. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1628.

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The injury characteristics of baby walkers were analyzed based on injury data about baby walkers from the National Electronic Injury Surveillance System (NEISS) of U.S. and the National Injury Related Products Surveillance System (NISS) of China. The main causes for the baby walkers injuries were identified by testing and evaluation, including insufficient anti-collision space, low designed static and dynamic strength, substandard outsourced casters and the processing technique not in conformity with the requirements for accessible parts. Such suggestions were put forward as increasing the anti-collision space, enhancing the strength of structural materials, performing stricter acceptance check for the outsourced casters and confirming the process inspection of semi-finished products.

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42

Cheng, Liting, Jian Dai, Zhiguo Yang, Wei Qian, Wei Wang, Ziyi Wang, and Zhongwei Gao. "Variation of larch wood property indexes based on nondestructive testing data." BioResources 15, no. 2 (March 10, 2020): 2906–23. http://dx.doi.org/10.15376/biores.15.2.2906-2923.

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To quickly evaluate the material properties of ancient wooden structure members on site, the larch species of northeastern China was used as the research object, and the nondestructive testing method of the stress wave and the micro-drill resistance meter were used to measure it. The variation laws of the larch wood’s cross and longitudinal property indexes were determined. According to the variation law of material property indexes, the detection divisions under the nondestructive testing technology of stress wave and micro-drill resistance instruments were divided, which provides a basis for improving the accuracy of on-site nondestructive testing. From the comparison of shade and light side data, it was found that there was little difference in material properties indexes. According to the change trend of larch wood with density, the pith, juvenile wood, mature wood, and overmature wood were divided, which provided a reference value for the study of wood growth laws.

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43

Grokhotov, M. "TESTING OF THE PHOTOTHERMAL IGNITION DESIGN MODEL BASED ON EXPERIMENTAL DATA." Fire and Emergencies: prevention, elimination, no. 3 (2017): 75–78. http://dx.doi.org/10.25257/fe.2017.3.75-78.

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44

Téllez-Girón, R., K. Ergang, and A. Hunger. "A combined testing approach for instruction-set-based data processing devices." Microprocessing and Microprogramming 20, no. 1-3 (April 1987): 147–52. http://dx.doi.org/10.1016/0165-6074(87)90133-5.

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45

Jarmulak, Jacek, Eugene J. H. Kerckhoffs, and Peter-Paul van’t Veen. "Case-based reasoning for interpretation of data from non-destructive testing." Engineering Applications of Artificial Intelligence 14, no. 4 (August 2001): 401–17. http://dx.doi.org/10.1016/s0952-1976(01)00026-4.

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46

Iyengar, Satish G., Pramod K. Varshney, and Thyagaraju Damarla. "A Parametric Copula-Based Framework for Hypothesis Testing Using Heterogeneous Data." IEEE Transactions on Signal Processing 59, no. 5 (May 2011): 2308–19. http://dx.doi.org/10.1109/tsp.2011.2105483.

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47

Oya, J. R. G., F. Munoz, A. Torralba, A. Jurado, A. J. Garrido, and J. Banos. "Data Acquisition System Based on Subsampling for Testing Wideband Multistandard Receivers." IEEE Transactions on Instrumentation and Measurement 60, no. 9 (September 2011): 3234–37. http://dx.doi.org/10.1109/tim.2011.2128710.

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48

Arici, Tarik, Toygar Akgun, and Yucel Altunbasak. "A prediction error-based hypothesis testing method for sensor data acquisition." ACM Transactions on Sensor Networks 2, no. 4 (November 2006): 529–56. http://dx.doi.org/10.1145/1218556.1218560.

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49

La Rosa, Patricio S., J. Paul Brooks, Elena Deych, Edward L. Boone, David J. Edwards, Qin Wang, Erica Sodergren, George Weinstock, and William D. Shannon. "Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data." PLoS ONE 7, no. 12 (December 20, 2012): e52078. http://dx.doi.org/10.1371/journal.pone.0052078.

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50

King, Maxwell L., and Merran A. Evans. "Testing for Block Effects in Regression Models Based on Survey Data." Journal of the American Statistical Association 81, no. 395 (September 1986): 677–79. http://dx.doi.org/10.1080/01621459.1986.10478320.

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