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Journal articles on the topic 'Random testing'

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Published: 4 June 2021
Last updated: 5 February 2022

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1

CHAN, KWOK PING, TSONG YUEH CHEN, and DAVE TOWEY. "RESTRICTED RANDOM TESTING: ADAPTIVE RANDOM TESTING BY EXCLUSION." International Journal of Software Engineering and Knowledge Engineering 16, no. 04 (August 2006): 553–84. http://dx.doi.org/10.1142/s0218194006002926.

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Restricted Random Testing (RRT) is a new method of testing software that improves upon traditional Random Testing (RT) techniques. Research has indicated that failure patterns (portions of an input domain which, when executed, cause the program to fail or reveal an error) can influence the effectiveness of testing strategies. For certain types of failure patterns, it has been found that a widespread and even distribution of test cases in the input domain can be significantly more effective at detecting failure compared with ordinary RT. Testing methods based on RT, but which aim to achieve even and widespread distributions, have been called Adaptive Random Testing (ART) strategies. One implementation of ART is RRT. RRT uses exclusion zones around executed, but non-failure-causing, test cases to restrict the regions of the input domain from which subsequent test cases may be drawn. In this paper, we introduce the motivation behind RRT, explain the algorithm and detail some empirical analyses carried out to examine the effectiveness of the method. Two versions of RRT are presented: Ordinary RRT (ORRT) and Normalized RRT (NRRT). The two versions share the same fundamental algorithm, but differ in their treatment of non-homogeneous input domains. Investigations into the use of alternative exclusion shapes are outlined, and a simple technique for reducing the computational overheads of RRT, prompted by the alternative exclusion shape investigations, is also explained. The performance of RRT is compared with RT and another ART method based on maximized minimum test case separation (DART), showing excellent improvement over RT and a very favorable comparison with DART.
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2

Wu, Huayao, Changhai Nie, Justyna Petke, Yue Jia, and Mark Harman. "An Empirical Comparison of Combinatorial Testing, Random Testing and Adaptive Random Testing." IEEE Transactions on Software Engineering 46, no. 3 (March 1, 2020): 302–20. http://dx.doi.org/10.1109/tse.2018.2852744.

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3

Linklater, Dawn R. "Random breath‐testing." Medical Journal of Australia 142, no. 7 (April 1985): 427. http://dx.doi.org/10.5694/j.1326-5377.1985.tb133178.x.

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4

Landauer, A. A., and J. R. Johnstone. "Random breath‐testing." Medical Journal of Australia 142, no. 4 (February 1985): 283. http://dx.doi.org/10.5694/j.1326-5377.1985.tb113352.x.

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5

Chen, Tsong Yueh, and Robert Merkel. "Quasi-Random Testing." IEEE Transactions on Reliability 56, no. 3 (September 2007): 562–68. http://dx.doi.org/10.1109/tr.2007.903293.

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6

Loo, PS, and WK Tsai. "Random testing revisited." Information and Software Technology 30, no. 7 (September 1988): 402–17. http://dx.doi.org/10.1016/0950-5849(88)90037-7.

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7

Nie, Changhai, Huayao Wu, Xintao Niu, Fei-Ching Kuo, Hareton Leung, and Charles J. Colbourn. "Combinatorial testing, random testing, and adaptive random testing for detecting interaction triggered failures." Information and Software Technology 62 (June 2015): 198–213. http://dx.doi.org/10.1016/j.infsof.2015.02.008.

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8

Liu, Huai, Fei-Ching Kuo, and Tsong Yueh Chen. "Comparison of adaptive random testing and random testing under various testing and debugging scenarios." Software: Practice and Experience 42, no. 8 (September 2, 2011): 1055–74. http://dx.doi.org/10.1002/spe.1113.

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9

Mrozek, Ireneusz, and Vyacheslav Yarmolik. "Multiple Controlled Random Testing*." Fundamenta Informaticae 144, no. 1 (March 4, 2016): 23–43. http://dx.doi.org/10.3233/fi-2016-1322.

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10

Nakamura, Tomomichi, and Michael Small. "Testing for random walk." Physics Letters A 362, no. 2-3 (February 2007): 189–97. http://dx.doi.org/10.1016/j.physleta.2006.10.018.

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11

Uretsky, Yan S. "Random vibrations testing device." Journal of the Acoustical Society of America 79, no. 5 (May 1986): 1643. http://dx.doi.org/10.1121/1.393748.

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12

Parsa, Saeed, and Esmaee Nikravan. "Hybrid adaptive random testing." International Journal of Computing Science and Mathematics 11, no. 3 (2020): 209. http://dx.doi.org/10.1504/ijcsm.2020.10028215.

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13

Nikravan, Esmaeel, and Saeed Parsa. "Hybrid adaptive random testing." International Journal of Computing Science and Mathematics 11, no. 3 (2020): 209. http://dx.doi.org/10.1504/ijcsm.2020.106694.

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14

Liu, Huai, and Tsong Yueh Chen. "Randomized Quasi-Random Testing." IEEE Transactions on Computers 65, no. 6 (June 1, 2016): 1896–909. http://dx.doi.org/10.1109/tc.2015.2455981.

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15

Gloag, D. "Random breath testing now." BMJ 302, no. 6774 (February 23, 1991): 430. http://dx.doi.org/10.1136/bmj.302.6774.430.

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16

Brock, Margaret F., and Raymond C. Roy. "Random Urine Drug Testing." Anesthesia & Analgesia 108, no. 2 (February 2009): 676. http://dx.doi.org/10.1213/ane.0b013e3181901da6.

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17

Fitzsimons, Michael G., Keith Baker, Edward Lowenstein, and Warren Zapol. "Random Urine Drug Testing." Anesthesia & Analgesia 108, no. 2 (February 2009): 676–77. http://dx.doi.org/10.1213/ane.0b013e3181901db9.

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18

Chen, T. Y., F. C. Kuo, R. G. Merkel, and S. P. Ng. "Mirror adaptive random testing." Information and Software Technology 46, no. 15 (December 2004): 1001–10. http://dx.doi.org/10.1016/j.infsof.2004.07.004.

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19

David, R., A. Fuentes, and B. Courtois. "Random pattern testing versus deterministic testing of RAMs." IEEE Transactions on Computers 38, no. 5 (May 1989): 637–50. http://dx.doi.org/10.1109/12.24267.

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20

Morrison, Julie. "Random testing will protect patients." Nursing Standard 18, no. 28 (March 24, 2004): 31. http://dx.doi.org/10.7748/ns.18.28.31.s49.

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21

Tallberg, Christian. "Testing centralization in random graphs." Social Networks 26, no. 3 (July 2004): 205–19. http://dx.doi.org/10.1016/j.socnet.2004.02.001.

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22

Srinivasan, Ashok, Michael Mascagni, and David Ceperley. "Testing parallel random number generators." Parallel Computing 29, no. 1 (January 2003): 69–94. http://dx.doi.org/10.1016/s0167-8191(02)00163-1.

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23

Blackwell, Brenda Sims, and Harold G. Grasmick. "Random Drug Testing and Religion." Sociological Inquiry 67, no. 2 (April 1997): 135–50. http://dx.doi.org/10.1111/j.1475-682x.1997.tb00436.x.

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24

Caldicott, Peter J. "Distribution testing — sine or random?" Packaging Technology and Science 4, no. 5 (September 1991): 287–91. http://dx.doi.org/10.1002/pts.2770040509.

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25

Ntafos, Simeon. "On random and partition testing." ACM SIGSOFT Software Engineering Notes 23, no. 2 (March 1998): 42–48. http://dx.doi.org/10.1145/271775.271785.

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26

Shiller, Robert J., and Pierre Perron. "Testing the random walk hypothesis." Economics Letters 18, no. 4 (January 1985): 381–86. http://dx.doi.org/10.1016/0165-1765(85)90058-8.

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27

Hemerik, Jesse, and Jelle Goeman. "Exact testing with random permutations." TEST 27, no. 4 (November 30, 2017): 811–25. http://dx.doi.org/10.1007/s11749-017-0571-1.

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28

Gutjahr, W. J. "Partition testing vs. random testing: the influence of uncertainty." IEEE Transactions on Software Engineering 25, no. 5 (1999): 661–74. http://dx.doi.org/10.1109/32.815325.

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29

Pastor, Dominique, and Francois-Xavier Socheleau. "Random distortion testing with linear measurements." Signal Processing 145 (April 2018): 116–26. http://dx.doi.org/10.1016/j.sigpro.2017.11.017.

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30

Bird, Sheila M. "Random mandatory drugs testing of prisoners." Lancet 365, no. 9469 (April 2005): 1451–52. http://dx.doi.org/10.1016/s0140-6736(05)66400-8.

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31

Kirmani, Syed N. U. A., and Jean-Yves Dauxois. "Testing relative risk under random censoring." Statistics & Probability Letters 62, no. 1 (March 2003): 1–7. http://dx.doi.org/10.1016/s0167-7152(02)00355-3.

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32

Xiaodong Zhang, Wenlei Shan, and K. Roy. "Low-power weighted random pattern testing." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 19, no. 11 (2000): 1389–98. http://dx.doi.org/10.1109/43.892863.

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33

Barker, Lawrence, Henry Rolka, Deborah Rolka, and Cedric Brown. "Equivalence Testing for Binomial Random Variables." American Statistician 55, no. 4 (November 2001): 279–87. http://dx.doi.org/10.1198/000313001753272213.

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34

McCausland, William J., Clintin Davis-Stober, AAJ Marley, Sanghyuk Park, and Nicholas Brown. "Testing the Random Utility Hypothesis Directly." Economic Journal 130, no. 625 (July 19, 2019): 183–207. http://dx.doi.org/10.1093/ej/uez039.

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Abstract We test a set of inequalities in choice probabilities, shown to be necessary and sufficient for random utility by Falmagne (1978). We run an experiment in which each of 141 participants chooses six times from each doubleton or larger subset of a universe of five lotteries. We compute Bayes factors in favour of random utility, versus an alternative with unrestricted choice probabilities. There is strong evidence that a large majority of participants satisfy random utility; however, there is strong evidence against random utility for four participants. Results are fairly robust to the choice of prior.
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35

Breunig, Christoph, and Stefan Hoderlein. "Specification testing in random coefficient models." Quantitative Economics 9, no. 3 (2018): 1371–417. http://dx.doi.org/10.3982/qe757.

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36

Solomon, R., E. Chamberlain, M. Abdoullaeva, and B. Tinholt. "Random Breath Testing: A Canadian Perspective." Traffic Injury Prevention 12, no. 2 (March 31, 2011): 111–19. http://dx.doi.org/10.1080/15389588.2010.533315.

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37

Sundar, V. S., and C. S. Manohar. "Random vibration testing with controlled samples." Structural Control and Health Monitoring 21, no. 10 (February 7, 2014): 1269–83. http://dx.doi.org/10.1002/stc.1646.

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38

Haché, A., L. Tkeshelashvili, M. Diem, and K. Busch. "Testing random numbers with periodic structures." Europhysics Letters (EPL) 73, no. 2 (January 2006): 225–31. http://dx.doi.org/10.1209/epl/i2005-10388-3.

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39

Chen, Tsong Yueh, Fei-Ching Kuo, Huai Liu, and W. Eric Wong. "Code Coverage of Adaptive Random Testing." IEEE Transactions on Reliability 62, no. 1 (March 2013): 226–37. http://dx.doi.org/10.1109/tr.2013.2240898.

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40

Es, T. N., and H. Martens. "Testing adequacy of linear random models." Statistics 18, no. 3 (January 1987): 323–31. http://dx.doi.org/10.1080/02331888708802023.

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41

Junpeng Lv, Hai Hu, Kai-Yuan Cai, and Tsong Yueh Chen. "Adaptive and Random Partition Software Testing." IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, no. 12 (December 2014): 1649–64. http://dx.doi.org/10.1109/tsmc.2014.2318019.

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42

Peralta, Ren�, and Victor Shoup. "Primality testing with fewer random bits." Computational Complexity 3, no. 4 (December 1993): 355–67. http://dx.doi.org/10.1007/bf01275488.

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43

Heckman, James J., Daniel Schmierer, and Sergio Urzua. "Testing the correlated random coefficient model." Journal of Econometrics 158, no. 2 (October 2010): 177–203. http://dx.doi.org/10.1016/j.jeconom.2010.01.005.

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44

Derfel, G., A. Y. Gordon, and S. Molchanov. "Random matrices over Zp and testing of random number generators (RNG's)." Random Operators and Stochastic Equations 12, no. 1 (January 1, 2004): 1–10. http://dx.doi.org/10.1515/156939704323067799.

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45

Koubek, Antonín, Zbyněk Pawlas, Tim Brereton, Björn Kriesche, and Volker Schmidt. "Testing the random field model hypothesis for random marked closed sets." Spatial Statistics 16 (May 2016): 118–36. http://dx.doi.org/10.1016/j.spasta.2016.03.001.

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46

P., Saravanan. "Bayesian Software Failure Probability based Time-Invariant Tanimoto Random Testing for Software Quality Management." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (February 13, 2020): 473–85. http://dx.doi.org/10.5373/jardcs/v12sp1/20201094.

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47

Khanduri, Prashant, Dominique Pastor, Vinod Sharma, and Pramod K. Varshney. "Truncated Sequential Non-Parametric Hypothesis Testing Based on Random Distortion Testing." IEEE Transactions on Signal Processing 67, no. 15 (August 1, 2019): 4027–42. http://dx.doi.org/10.1109/tsp.2019.2923140.

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48

Sun, Fu Qiang, Xiao Yang Li, Jing Rui Zhang, and Tong Min Jiang. "Step-Stress Accelerated Random Vibration Life Testing." Advanced Materials Research 118-120 (June 2010): 606–10. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.606.

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In order to reduce the sample size and test time, a step-stress accelerated random vibration life testing (ARVLT) method is presented. And the method of parameter estimation is also proposed. Through this testing method, the exaggeration factor K can be obtained rapidly, so that the result can be used as the technical basis of conducting an equivalent accelerated vibration testing of the product. In addition, the reliability of the product under the normal operation stress level can be extrapolated.
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49

Hasan, J. H., A. M. Alshraideh, and A. B. Mahafzah. "Branch Coverage Testing Using Anti-Random Technique." i-manager's Journal on Software Engineering 8, no. 2 (December 15, 2013): 7–19. http://dx.doi.org/10.26634/jse.8.2.2534.

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50

Konopka, Jörg, and Norbert Schmitz. "Redundant observations at testing i.i.d. random variables." Statistical Papers 43, no. 4 (October 2002): 595–602. http://dx.doi.org/10.1007/s00362-002-0127-6.

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