Relevant bibliographies by topics / Random testing

Academic literature on the topic 'Random testing'

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Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Random testing"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Random testing"

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Oftedal, Kristian. "Random Testing versus Partition Testing." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13985.

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The difference between Partition Testing and Random Testing has been thoroughlyinvestigated theoretically. In this thesis we present a practical study ofthe differences between random testing and partition testing. Thestudy is performed on the open-source project Buddi with JUnit and Randoop as test tools. The comparisonis made with respect to coverage rate and fault rate. The resultsare discussed and analyzed. The observed differences are statisticallysignificant at the 10% level with respect to coverage rate, in favour ofpartition testing, and not statistically significant at the 10% level withrespect to the fault rate.
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Pacheco, Carlos Ph D. Massachusetts Institute of Technology. "Directed random testing." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53297.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 155-162).
Random testing can quickly generate many tests, is easy to implement, scales to large software applications, and reveals software errors. But it tends to generate many tests that are illegal or that exercise the same parts of the code as other tests, thus limiting its effectiveness. Directed random testing is a new approach to test generation that overcomes these limitations, by combining a bottom-up generation of tests with runtime guidance. A directed random test generator takes a collection of operations under test and generates new tests incrementally, by randomly selecting operations to apply and finding arguments from among previously-constructed tests. As soon as it generates a new test, the generator executes it, and the result determines whether the test is redundant, illegal, error-revealing, or useful for generating more tests. The technique outputs failing tests pointing to potential errors that should be corrected, and passing tests that can be used for regression testing. The thesis also contributes auxiliary techniques that post-process the generated tests, including a simplification technique that transforms a, failing test into a smaller one that better isolates the cause of failure, and a branch-directed test generation technique that aims to increase the code coverage achieved by the set of generated tests. Applied to 14 widely-used libraries (including the Java JDK and the core .NET framework libraries), directed random testing quickly reveals many serious, previously unknown errors in the libraries. And compared with other test generation tools (model checking, symbolic execution, and traditional random testing), it reveals more errors and achieves higher code coverage.
(cont.) In an industrial case study, a test team at Microsoft using the technique discovered in fifteen hours of human effort as many errors as they typically discover in a person-year of effort using other testing methods.
by Carlos Pacheco.
Ph.D.
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Kuo, Fei-Ching, and n/a. "On adaptive random testing." Swinburne University of Technology, 2006. http://adt.lib.swin.edu.au./public/adt-VSWT20061109.091517.

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Adaptive random testing (ART) has been proposed as an enhancement to random testing for situations where failure-causing inputs are clustered together. The basic idea of ART is to evenly spread test cases throughout the input domain. It has been shown by simulations and empirical analysis that ART frequently outperforms random testing. However, there are some outstanding issues on the cost-effectiveness and practicality of ART, which are the main foci of this thesis. Firstly, this thesis examines the basic factors that have an impact on the faultdetection effectiveness of adaptive random testing, and identifies favourable and unfavourable conditions for ART. Our study concludes that favourable conditions for ART occur more frequently than unfavourable conditions. Secondly, since all previous studies allow duplicate test cases, there has been a concern whether adaptive random testing performs better than random testing because ART uses fewer duplicate test cases. This thesis confirms that it is the even spread rather than less duplication of test cases which makes ART perform better than RT. Given that the even spread is the main pillar of the success of ART, an investigation has been conducted to study the relevance and appropriateness of several existing metrics of even spreading. Thirdly, the practicality of ART has been challenged for nonnumeric or high dimensional input domains. This thesis provides solutions that address these concerns. Finally, a new problem solving technique, namely, mirroring, has been developed. The integration of mirroring with adaptive random testing has been empirically shown to significantly increase the cost-effectiveness of ART. In summary, this thesis significantly contributes to both the foundation and the practical applications of adaptive random testing.
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Kuo, Fei-Ching. "On adaptive random testing." Australasian Digital Thesis Program, 2006. http://adt.lib.swin.edu.au/public/adt-VSWT20061109.091517.

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Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, 2006.
A thesis submitted for the degree of PhD, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2006. Typescript. Bibliography: p. 126-133.
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Mitran, Cosmin. "Guided random-based testing strategies." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Department of Computer Science, 2007. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=328.

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Ciupa, Ilinca. "Strategies for random contract-based testing /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=18143.

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Ahmad, Mian Asbat. "New strategies for automated random testing." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/7981/.

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The ever increasing reliance on software-intensive systems is driving research to discover software faults more effectively and more efficiently. Despite intensive research, very few approaches have studied and used knowledge about fault domains to improve the testing or the feedback given to developers. The present thesis addresses this shortcoming: it leverages fault co-localization in a new random testing strategy called Dirt Spot Sweep- ing Random (DSSR), and it presents two new strategies: Automated Discovery of Failure Domain (ADFD) and Automated Discovery of Failure Domain+ (ADFD+). These improve the feedback given to developers by deducing more information about the failure domain (i.e. point, block, strip) in an automated way. The DSSR strategy adds the value causing the failure and its neighbouring values to the list of interesting values for exploring the underlying failure domain. The comparative evaluation showed significantly better performance of DSSR over Random and Random+ strategies. The ADFD strategy finds failures and failure domains and presents the pass and fail domains in graphical form. The results obtained by evaluating error-seeded numerical programs indicated highly effective performance of the ADFD strategy. The ADFD+ strategy is an extended version of ADFD strategy with respect to algorithm and graphical presentation of failure domains. In comparison with Randoop, ADFD+ strategy successfully detected all failures and failure domains while Randoop identified individual failures but could not detect failure domains. The ADFD and ADFD+ techniques were enhanced by integration of the automatic invariant detector Daikon, and the precision of identifying failure domains was determined through extensive experimental evaluation of real world Java projects contained in a database, namely Qualitas Corpus. The analyses of results, cross-checked by manual testing indicated that the ADFD and ADFD+ techniques are highly effective in providing assistance but are not an alternative to manual testing.
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Pesaresi, Emanuele. "Leptokurtic signals in random control vibration testing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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In several industrial sectors, some components are subjected to mechanical vibrations which may lead to a premature failure. To ensure that they operate properly during their service life, the utilization of qualification tests has been consolidated over the years. It is often required to carry out accelerated tests for obvious reasons as feasibility and cost: the aim is to limit the duration of tests. The Test Tailoring procedure requires an appropriate definition for vibratory test profiles to be utilized as an excitation in terms of motion generated by vibrating tables or shakers. The synthesis of such profiles requires that signals be measured in real environments and then that their most important characteristics be reproduced in a laboratory, in particular their spectral content and damage potential.The conventional procedures permit the synthesis of an accelerated test profile in terms of a Power Spectral Density, which is characterized by a Gaussian distribution of the corresponding timeseries values. Such a kind of synthesis might be unfit to represent the real environment signal taken as a reference, owing to the latter’s usual non-Gaussianity. As a consequence, reliability could be compromised since the “nature” of the real signal is not preserved. Typical examples of non-Gaussian signals coming forth in real applications are the so-called Leptokurtic signals, whose high amplitude peaks originate a strongly non Gaussian probability distribution. A parameter called kurtosis is often employed to represent the number and severity of the peaks of the signal. A common reference is made to “kurtosis control” whenever it is required that the synthesized and the measured signal have not only the same spectral content but the same kurtosis value as well. In this work some novel Mission Synthesis algorithms are proposed, which generate test profiles by controlling precisely the kurtosis value and complying with the spectral content of the reference signal.
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Liu, Ning Lareina. "A study on improving adaptive random testing." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36428061.

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Merkel, Robert Graham, and robert merkel@benambra org. "Analysis and enhancements of adaptive random testing." Swinburne University of Technology, 2005. http://adt.lib.swin.edu.au./public/adt-VSWT20050804.144747.

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Random testing is a standard software testing method. It is a popular method for reli-ability assessment, but its use for debug testing has been opposed by some authorities. Random testing does not use any information to guide test case selection, and so, it is argued, testing is less likely to be effective than other methods. Based on the observation that failures often cluster in contiguous regions, Adaptive Random Testing (ART) is a more effective random testing method. While retaining random selection of test cases, selection is guided by the idea that tests should be widely spread throughout the input domain. A simple way to implement this concept, FSCS-ART, involves randomly generating a number of candidates, and choosing the candidate most widely spread from any already-executed test. This method has already shown to be up to 50% more effective than random testing. This thesis examines a number of theoretical and practical issues related to ART. Firstly, an theoretical examination of the scope of adaptive methods to improve testing effectiveness is conducted. Our results show that the maximum improvement in failure detection effectiveness possible is only 50% - so ART performs close to this limit on many occasions. Secondly, the statistical validity of the previous empirical results is examined. A mathematical analysis of the sampling distribution of the various failure-detection effectiveness methods shows that the measure preferred in previous studies has a slightly unusual distribution known as the geometric distribution, and that that it and other measures are likely to show high variance, requiring very large sample sizes for accurate comparisons. A potential limitation of current ART methods is the relatively high selection overhead. A number of methods to obtain lower overheads are proposed and evaluated, involving a less-strict randomness or wide-spreading criterion. Two methods use dynamic, as-needed partitioning to divide the input domain, spreading test cases throughout the partitions as required. Another involves using a class of numeric sequences called quasi-random sequences. Finally, a more efficient implementation of the existing FSCS-ART method is proposed using the mathematical structure known as the Voronoi diagram. Finally, the use of ART on programs whose input is non-numeric is examined. While existing techniques can be used to generate random non-numeric candidates, a criterion for 'wide spread' is required to perform ART effectively. It is proposed to use the notion of category-partition as such a criterion.
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Books on the topic "Random testing"

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Hamdioui, Said. Testing Static Random Access Memories. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-6706-3.

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Petlin, Oleg Alexandrovich. Random testing of asynchronous VLSI circuits. Manchester: University of Manchester, 1994.

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Davis, Gerald J. Conducting random drug and alcohol testing. Alexandria, VA: American Trucking Associations, 1994.

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René, David. Random testing of digital circuits: Theory & application. New York: Marcel Dekker, 1998.

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Random testing of digital circuits: Theory and applications. New York: Marcel Dekker, 1998.

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James-Burdumy, Susanne. The effectiveness of mandatory-random student drug testing. Washington, DC: U.S. Dept. of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, 2010.

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Kanad, Chakraborty, ed. Testing and testable design of high-density random-access memories. Boston, Mass: Kluwer Academic, 1996.

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I͡Armolik, V. N. Generation and application of pseudorandom sequences for random testing. Chichester [West Sussex]: Wiley, 1988.

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IEEE International Workshop on Memory Technology, Design, and Testing (1998 San Jose, California). Memory technology, design and testing: Proceedings : International Workshop on Memory Technology, Design, and Testing. Los Alamitos, California: IEEE Computer Society Press, 1998.

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Hughes, William O. Vibration testing of an operating Stirling convertor. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2000.

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Book chapters on the topic "Random testing"

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Chan, Kwok Ping, Tsong Yueh Chen, and Dave Towey. "Restricted Random Testing." In Software Quality — ECSQ 2002, 321–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47984-8_35.

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Caddy, Tom. "Random Number Testing." In Encyclopedia of Cryptography and Security, 1026–27. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_221.

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Chen, T. Y., H. Leung, and I. K. Mak. "Adaptive Random Testing." In Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making, 320–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30502-6_23.

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Chan, Kwok Ping, Tsong Yueh Chen, and Dave Towey. "Good Random Testing." In Lecture Notes in Computer Science, 200–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24841-5_16.

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Mrozek, Ireneusz. "Controlled Random Testing." In Multi-run Memory Tests for Pattern Sensitive Faults, 29–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91204-2_4.

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Kneusel, Ronald T. "Testing Pseudorandom Generators." In Random Numbers and Computers, 115–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77697-2_4.

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L’Ecuyer, Pierre, and Peter Hellekalek. "Random Number Generators: Selection Criteria and Testing." In Random and Quasi-Random Point Sets, 223–65. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1702-2_5.

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Chan, Kwok Ping, Tsong Yueh Chen, and Dave Towey. "Normalized Restricted Random Testing." In Lecture Notes in Computer Science, 368–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44947-7_28.

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Gaudel, Marie-Claude. "Counting for Random Testing." In Testing Software and Systems, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24580-0_1.

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Mrozek, Ireneusz. "Multiple Controlled Random Testing." In Multi-run Memory Tests for Pattern Sensitive Faults, 87–100. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91204-2_7.

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Conference papers on the topic "Random testing"

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Gerlich, Rainer, Ralf Gerlich, and Thomas Boll. "Random testing." In the 2nd international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1292414.1292424.

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Chen, T. Y., and F. C. Kuo. "Is adaptive random testing really better than random testing." In the 1st international workshop. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1145735.1145745.

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Chen, T. Y. "Adaptive Random Testing." In 2008 Eighth International Conference on Quality Software (QSIC). IEEE, 2008. http://dx.doi.org/10.1109/qsic.2008.22.

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Chen, Tsong Yueh, and Robert Merkel. "Quasi-random testing." In the 20th IEEE/ACM international Conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1101908.1101957.

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Arcuri, Andrea, and Lionel Briand. "Adaptive random testing." In the 2011 International Symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001420.2001452.

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Chen, Tsong Yueh, Fei-Ching Kuo, Huai Liu, and W. Eric Wong. "Does Adaptive Random Testing Deliver a Higher Confidence than Random Testing?" In 2008 Eighth International Conference on Quality Software (QSIC). IEEE, 2008. http://dx.doi.org/10.1109/qsic.2008.23.

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Godefroid, Patrice. "Random testing for security." In the 2nd international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1292414.1292416.

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Chen, T. Y., F. C. Kuo, R. G. Merkel, and S. P. Ng. "Mirror adaptive random testing." In Third International Conference on Quality Software, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/qsic.2003.1319079.

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Chan, Kwok, T. y. Chen, and Dave Towey. "Probabilistic Adaptive Random Testing." In 2006 Sixth International Conference on Quality Software (QSIC'06). IEEE, 2006. http://dx.doi.org/10.1109/qsic.2006.48.

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L'Ecuyer, Pierre. "Testing random number generators." In the 24th conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/167293.167354.

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Reports on the topic "Random testing"

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Schmierer, Daniel, James Heckman, and Sergio Urzua. Testing the correlated random coefficient model. Institute for Fiscal Studies, April 2010. http://dx.doi.org/10.1920/wp.cem.2010.1010.

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Woods, Robert L. Random Drug Testing of Federal Employees. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada217954.

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Heckman, James, Daniel Schmierer, and Sergio Urzua. Testing the Correlated Random Coefficient Model. Cambridge, MA: National Bureau of Economic Research, October 2009. http://dx.doi.org/10.3386/w15463.

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Tarman, Michael F. Random Drug Testing of Army Civilian Employees. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada209622.

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Kitamura, Yuichi, and Jorg Stoye. Nonparametric analysis of random utility models: testing. Institute for Fiscal Studies, August 2013. http://dx.doi.org/10.1920/wp.cem.2013.3613.

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Collins, Joseph C. Testing, Selection, and Implementation of Random Number Generators. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada486379.

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Boyle, James P. Daily Random Urinalysis Testing: Consequences of Deterrence Functions. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada294816.

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Shiller, Robert, and Pierre Perron. Testing the Random Walk Hypothesis: Power versus Frequency of Observation. Cambridge, MA: National Bureau of Economic Research, April 1985. http://dx.doi.org/10.3386/t0045.

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Eisele, Gerhard R., and Cameron W. Coates. Guide to Urine Specimen Collection for Random Drug Testing for the MVD-IT. Office of Scientific and Technical Information (OSTI), May 2014. http://dx.doi.org/10.2172/1132969.

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Tavik, Gregory C. Testing the One-Port Random Access Memory (1PRAM) Module of TRW's CPUAX Signal Processing Superchip. Fort Belvoir, VA: Defense Technical Information Center, April 1991. http://dx.doi.org/10.21236/ada234127.

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