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Journal articles on the topic 'Random testing'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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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 (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 (2020): 302–20. http://dx.doi.org/10.1109/tse.2018.2852744.

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3

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

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4

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

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5

Chen, Tsong Yueh, and Robert Merkel. "Quasi-Random Testing." IEEE Transactions on Reliability 56, no. 3 (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 (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 (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 (2016): 23–43. http://dx.doi.org/10.3233/fi-2016-1322.

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10

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

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11

Uretsky, Yan S. "Random vibrations testing device." Journal of the Acoustical Society of America 79, no. 5 (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

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

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15

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

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16

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

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17

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

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18

Kemnitz, G. "GUARDBANDS IN RANDOM TESTING." Proceedings of the Estonian Academy of Sciences. Engineering 3, no. 4 (1997): 260. http://dx.doi.org/10.3176/eng.1997.4.03.

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19

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

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20

Alamgir, Arbab, Abu Khari A'ain, Norlina Paraman, and Usman Ullah Sheikh. "Adaptive random testing with total cartesian distance for black box circuit under test." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (2020): 720–26. https://doi.org/10.11591/ijeecs.v20.i2.pp720-726.

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Testing and verification of digital circuits is of vital importance in electronics industry. Moreover, key designs require preservation of their intellectual property that might restrict access to the internal structure of circuit under test. Random testing is a classical solution to black box testing as it generates test patterns without using the structural implementation of the circuit under test. However, random testing ignores the importance of previously applied test patterns while generating subsequent test patterns. An improvement to random testing is Antirandom that diversifies every subsequent test pattern in the test sequence. Whereas, computational intensive process of distance calculation restricts its scalability for large input circuit under test. Fixed sized candidate set adaptive random testing uses predetermined number of patterns for distance calculations to avoid computational complexity. A combination of max-min distance with previously executed patterns is carried out for each test pattern candidate. However, the reduction in computational complexity reduces the effectiveness of test set in terms of fault coverage. This paper uses a total cartesian distance based approach on fixed sized candidate set to enhance diversity in test sequence. The proposed approach has a two way effect on the test pattern generation as it lowers the computational intensity along with enhancement in the fault coverage. Fault simulation results on ISCAS’85 and ISCAS’89 benchmark circuits show that fault coverage of the proposed method increases up to 20.22% compared to previous method.

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21

Noonan, Jack, and Anatoly Zhigljavsky. "Random and quasi-random designs in group testing." Journal of Statistical Planning and Inference 221 (December 2022): 29–54. http://dx.doi.org/10.1016/j.jspi.2022.02.006.

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22

Bilenko, Volodymyr, Mohammed Kadhim Rahma, and Valerii Hlukhov. "Testing of the Random Codes Generator of Embedded Crypto Protection System." Advances in Cyber-Physical Systems 7, no. 2 (2022): 70–75. http://dx.doi.org/10.23939/acps2022.02.070.

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The goal of the publication is to test the random codes generator of the built-in crypto-protection system Following the list of critical technologies in the production of weapons field and military equipment (following the Decree of the Cabinet of Ministers of Ukraine dated 30.08.2017 No. 600-r), an embedded microcontroller system for the protection of information for on-board equipment has been developed. A valuable stage of the system introduction is its research and testing. One of the stages of testing is the verification of the generator of random codes to use for the generation of encryption keys and digital signatures. Based on the previous works and research of the Kalyna algorithm, methods and tools for creating a random code generator have been studied to use in the built-in cryptographic data protection system for data encryption/decryption and for working with a digital signature. Means of checking generated random codes and comparing them with existing counterparts have been developed. The purpose of this article is to check the generator of random codes before using it in the built-in information protection system.

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23

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

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24

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

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25

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

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26

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

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27

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

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28

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

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29

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

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30

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

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31

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

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32

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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33

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 (2014): 1649–64. http://dx.doi.org/10.1109/tsmc.2014.2318019.

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34

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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35

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

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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 (2011): 111–19. http://dx.doi.org/10.1080/15389588.2010.533315.

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37

McCausland, William J., Clintin Davis-Stober, AAJ Marley, Sanghyuk Park, and Nicholas Brown. "Testing the Random Utility Hypothesis Directly." Economic Journal 130, no. 625 (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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38

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 (2013): 226–37. http://dx.doi.org/10.1109/tr.2013.2240898.

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39

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

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40

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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41

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

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42

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

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43

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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44

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

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45

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

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46

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

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47

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

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48

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 (2004): 1–10. http://dx.doi.org/10.1515/156939704323067799.

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49

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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50

Kaur, Kamaldeep, Sunil Khatri, Alok Mishra, and Rattan Datta. "Statistical Usage Testing at Different Levels of Testing." JUCS - Journal of Universal Computer Science 24, no. (12) (2018): 1800–1820. https://doi.org/10.3217/jucs-024-12-1800.

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Statistical Usage Testing (SUT) is the testing technique defined in Cleanroom Software Engineering model [Runeson, 93]. Cleanroom Software Engineering model is a theory based and team oriented model that is based on development and certification of software in increments using statistical quality control [Linger 96]. SUT is a black box testing technique and concentrates on how the software completes its required function from the user's perspective [Runeson, 93]. SUT is carried out by developing usage models and assigning usage probabilities. Testing is carried out on usage models by performing statistical tests which are random sequences [Trammel 95]. Statistical testing can be viewed as a statistical experiment where random test cases are selected from all the usage models [Trammel 95]. This paper demonstrates the process and benefits of applying SUT at different levels of testing. Levels of testing include Unit level, Integration level, System level and Acceptance level. SUT is generally performed at System level and Unit testing is not the part of SUT. Unit testing makes it easier to access code and debug human errors. Detecting errors at an early stage helps reducing cost and effort. The paper proposes to allow Unit testing in Cleanroom Software Engineering Model, thus making it more flexible and suitable for varied applications. Unit testing is essentially performed to ensure that the code is working correctly and meets the user specifications [istqb, 15]. Errors may also exist when modules are integrated because of interchange of data and control information between various modules. Integration testing is performed when the modules are combined together to check their behaviour and functionality after integration. Once the Integration testing phase gets successfully completed, System testing is performed on the whole system [test-institute, 15]. The paper makes use of Student record software to demonstrate the process of performing SUT at different levels. In addition to performing SUT at System level, this paper helps in understanding the advantages of applying SUT at Unit level and Integration level.

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