Relevant bibliographies by topics / Adaptive random testing

Academic literature on the topic 'Adaptive random testing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Adaptive random testing"

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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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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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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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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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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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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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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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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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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Chen, Tsong Yueh, Fei-Ching Kuo, and Huai Liu. "Adaptive random testing based on distribution metrics." Journal of Systems and Software 82, no. 9 (September 2009): 1419–33. http://dx.doi.org/10.1016/j.jss.2009.05.017.

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Dissertations / Theses on the topic "Adaptive random testing"

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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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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Liu, Ning Lareina, and 劉寧. "A study on improving adaptive random testing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36428061.

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Towey, David Peter. "Studies of different variations of Adaptive Random Testing." Thesis, View the Table of Contents & Abstract, 2006. http://sunzi.lib.hku.hk/hkuto/record/B3551212X.

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Liu, Huai. "On even spread of test cases in adaptive random testing." Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/40129.

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Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, 2008.
A thesis submitted for the degree of Doctor of Philosophy, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2008. Typescript. Bibliography: p. 107-123.

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Jian-Yu, Sun, and 孫建宇. "Fuzz Testing based on Adaptive Random Sequence Method." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/s582qq.

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碩士
國立交通大學
資訊科學與工程研究所
105
This thesis proposed a way to improve test case coverage in fuzz testing that combine Adaptive Random Sequence and Category–Partition-base Distance with American fuzzy lop (AFL). Finally, we applied this fuzzer to test several known vulnerabilities open source applications, and the coverage is improved.

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Liu, Chen-Wei, and 劉振維. "The Application of the Random-Threshold Generalized Graded Unfolding Model to Computerized Adaptive Testing and Computerized Classification Testing." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/70685125318945334528.

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碩士
國立中正大學
心理學所
97
The random-threshold generalized graded unfolding model (RTGGUM) was employed in this study(Wu & Wang, 2009). This model takes the randomness in the threshold over persons into account by treating it as random-effect and adding a random variable for each threshold. To investigate the applicability of the RTGGUM more thoroughly, it was further applied to both computerized adaptive testing (CAT) and computerized classification testing (CCT). By taking the random-threshold as another dimension, the RTGGUM-CAT can be regarded as a special case of multidimensional adaptive testing and both the latent trait and random thresholds were estimated simultaneously. On the other hand, the focus of RTGGUM-CCT is on the accuracy of classification and therefore the random thresholds are viewed as randomness. The results indicated that the existence of random thresholds would lead to inaccurate latent trait estimates, as well as increase the classification errors. Therefore, the reliability of latent trait estimates and the accuracy of classification are doubtful if the random thresholds were ignored. The related issues about the application of RTGGUM in CAT and CCT are discussed.

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Tappenden, Andrew. "Testing the Internet state management mechanism." Phd thesis, 2010. http://hdl.handle.net/10048/1055.

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This thesis presents an extensive survey of 100,000 websites as the basis for understanding the deployment of cookies across the Internet. The survey indicates cookie deployment on the Internet is approaching universal levels. The survey identifies the presence of P3P policies and dynamic web technologies as major predictors of cookie usage, and a number of significant relationships are established between the origin of the web application and cookie deployment. Large associations are identified between third-party persistent cookie usage and a countrys e-business environment. Cookie collection testing (CCT), a strategy for testing web applications, is presented. Cookies maintained in a browser are explored in light of anti random testing techniques, culminating in the definition of seeding vectors as the basis for a scalable test suite. Essentially CCT seeks to verify web application robustness against the modificationintentional or otherwiseof an application's internal state variables. Automation of CCT is outlined through the definition of test oracles and evaluation criterion. Evolutionary adaptive random (eAR) testing is proposed for application to the cookie collection testing strategy. A simulation study is undertaken to evaluate eAR against the current state-of-the-art in adaptive random testingfixed size candidate set, restricted random testing, quasi-random testing, and random testing. eAR is demonstrated to be superior to the other techniques for block pattern simulations. For fault patterns of increased complexity, eAR is shown to be comparable to the other methods. An empirical investigation of CCT is undertaken. CCT is demonstrated to reveal defects within web applications, and is found to have a substantial fault-triggering rate. Furthermore, CCT is demonstrated to interact with the underlying application, not just the technological platform upon which an application is implemented. Both seeding and generated vectors are found to be useful in triggering defects. A synergetic relationship is found to exist between the seeding and generated vectors with respect to distinct fault detection. Finally, a large significant relationship is established between structural and content similarity measures of web application responses, with a composite of the two similarity measures observed to be superior in the detection of faults.
Software Engineering and Intelligent Systems

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Book chapters on the topic "Adaptive random testing"

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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Koo, Dong-Su, and Young B. Park. "OFART: OpenFlow-Switch Adaptive Random Testing." In Lecture Notes in Electrical Engineering, 193–98. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5041-1_33.

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Chen, T. Y., De Hao Huang, and Zhi Quan Zhou. "Adaptive Random Testing Through Iterative Partitioning." In Reliable Software Technologies – Ada-Europe 2006, 155–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11767077_13.

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Koo, Dong-Su, and Young B. Park. "OFART: OpenFlow-Switch Adaptive Random Testing." In Advances in Computer Science and Ubiquitous Computing, 631–36. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3023-9_97.

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Mayer, Johannes. "Adaptive Random Testing by Bisection and Localization." In Formal Approaches to Software Testing, 72–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759744_6.

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Mayer, Johannes. "Adaptive Random Testing by Bisection with Restriction." In Formal Methods and Software Engineering, 251–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11576280_18.

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Mao, Chengying, Mengting Quan, Zhilei Chen, and Tsong Yueh Chen. "Adaptive Random Testing by Bisection and Comprehensive Distance." In Structured Object-Oriented Formal Language and Method, 328–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41418-4_22.

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Nie, Janping, Yueying Qian, and Nan Cui. "Enhanced Mirror Adaptive Random Testing Based on I/O Relation Analysis." In Advances in Intelligent and Soft Computing, 33–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-03718-4_5.

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Devika Rani Dhivya, K., and V. S. Meenakshi. "An Optimized Adaptive Random Partition Software Testing by Using Bacterial Foraging Algorithm." In Computational Vision and Bio Inspired Computing, 542–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71767-8_47.

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Iqbal, Muhammad Zohaib, Andrea Arcuri, and Lionel Briand. "Combining Search-Based and Adaptive Random Testing Strategies for Environment Model-Based Testing of Real-Time Embedded Systems." In Search Based Software Engineering, 136–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33119-0_11.

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Conference papers on the topic "Adaptive random testing"

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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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, 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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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., De Hao Huang, and F. C. Kuo. "Adaptive random testing by balancing." In the 2nd international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1292414.1292418.

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Mayer, Johannes. "Lattice-based adaptive random testing." In the 20th IEEE/ACM international Conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1101908.1101963.

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Patrick, Matthew, and Yue Jia. "Kernel Density Adaptive Random Testing." In 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2015. http://dx.doi.org/10.1109/icstw.2015.7107451.

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Mayer, Johannes. "Adaptive random testing with randomly translated failure region." In the 1st international workshop. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1145735.1145746.

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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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