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Relevant bibliographies by topics / Computer software testing; adaptive random testing; random testing / Journal articles

Journal articles on the topic 'Computer software testing; adaptive random testing; random testing'

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Published: 4 June 2021

Last updated: 7 February 2022

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1

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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2

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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3

CHEN, TSONG YUEH, FEI-CHING KUO, and ZHI QUAN ZHOU. "ON FAVOURABLE CONDITIONS FOR ADAPTIVE RANDOM TESTING." International Journal of Software Engineering and Knowledge Engineering 17, no. 06 (December 2007): 805–25. http://dx.doi.org/10.1142/s0218194007003501.

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Recently, adaptive random testing (ART) has been developed to enhance the fault-detection effectiveness of random testing (RT). It has been known in general that the fault-detection effectiveness of ART depends on the distribution of failure-causing inputs, yet this understanding is in coarse terms without precise details. In this paper, we conduct an in-depth investigation into the factors related to the distribution of failure-causing inputs that have an impact on the fault-detection effectiveness of ART. This paper gives a comprehensive analysis of the favourable conditions for ART. Our study contributes to the knowledge of ART and provides useful information for testers to decide when it is more cost-effective to use ART.

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CAI, KAI-YUAN, TSONG YUEH CHEN, YONG-CHAO LI, YUEN TAK YU, and LEI ZHAO. "ON THE ONLINE PARAMETER ESTIMATION PROBLEM IN ADAPTIVE SOFTWARE TESTING." International Journal of Software Engineering and Knowledge Engineering 18, no. 03 (May 2008): 357–81. http://dx.doi.org/10.1142/s0218194008003696.

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Software cybernetics is an emerging area that explores the interplay between software and control. The controlled Markov chain (CMC) approach to software testing supports the idea of software cybernetics by treating software testing as a control problem, where the software under test serves as a controlled object modeled by a controlled Markov chain and the software testing strategy serves as the corresponding controller. The software under test and the corresponding software testing strategy form a closed-loop feedback control system. The theory of controlled Markov chains is used to design and optimize the testing strategy in accordance with the testing/reliability goal given explicitly and a priori. Adaptive software testing adjusts and improves software testing strategy online by using the testing data collected in the course of software testing. In doing so, the online parameter estimations play a key role. In this paper, we study the effects of genetic algorithm and the gradient method for doing online parameter estimation in adaptive software testing. We find that genetic algorithm is effective and does not require prior knowledge of the software parameters of concern. Although genetic algorithm is computationally intensive, it leads the adaptive software testing strategy to an optimal software testing strategy that is determined by optimizing a given testing goal, such as minimizing the total cost incurred for removing a given number of defects. On the other hand, the gradient method is computationally favorable, but requires appropriate initial values of the software parameters of concern. It may lead, or fail to lead, the adaptive software testing strategy to an optimal software testing strategy, depending on whether the given initial parameter values are appropriate or not. In general, the genetic algorithm should be used instead of the gradient method in adaptive software testing. Simulation results show that adaptive software testing does work and outperforms random testing.

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5

Thu Nguyet, Phan Thi, and Muslem Daud. "Computer Adaptive Test Development To Assess Students’ Psychology." JURNAL SERAMBI ILMU 22, no. 1 (March 22, 2021): 139–49. http://dx.doi.org/10.32672/si.v22i1.2760.

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Stress becomes a significantly serious issue among university students and we need efficient tools to understand it more. The aim of present study is to develop a Computerized Adaptive Testing (CAT) to measure the mentioned stress, as pioneer project in Vietnam. In this vein, an item bank of 68 items has been constructed, which is based on Likert Polytomous Scales through five subdomains: behavior, academic performance, family, lecturer and finance. The sampling of the survey is large. It has assessed 2,085 students (704 males and 1,381 females). Multidimensional Random Coefficients Multinomial Logit (MRCML) Model is applied to develop Multidimensional Stress Scales and Computerized Adaptive Testing procedure. The result findings indicate that Multidimensional Random Coefficients Multinomial Logit (MRCML) can be used to develop new scale with psychometric properties. Indicated by various fit criteria MNSQ, standard errors, Z (t-test) implemented in software ConQuest. The subdomain has a good reliability (from .857 to .798). As respect to CATs, a simulated experiment based on the empirical data is applied to evaluate the performance of the proposed computerized adaptive testing. The standard error of the estimated stress proficiencies are reported in this study. The 68 items stress data appropriate fit the Multidimensional model applied.

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6

Omari, Michael, Jinfu Chen, Robert French-Baidoo, and Yunting Sun. "A Proactive Approach to Test Case Selection — An Efficient Implementation of Adaptive Random Testing." International Journal of Software Engineering and Knowledge Engineering 30, no. 08 (August 2020): 1169–98. http://dx.doi.org/10.1142/s0218194020500308.

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Fixed Sized Candidate Set (FSCS) is the first of a series of methods proposed to enhance the effectiveness of random testing (RT) referred to as Adaptive Random Testing methods or ARTs. Since its inception, test case generation overheads have been a major drawback to the success of ART. In FSCS, the bulk of this cost is embedded in distance computations between a set of randomly generated candidate test cases and previously executed but unsuccessful test cases. Consequently, FSCS is caught in a logical trap of probing the distances between every candidate and all executed test cases before the best candidate is determined. Using data mining, however, we discovered that about 50% of all valid test cases are encountered much earlier in the distance computations process but without any benefit of a hindsight, FSCS is unable to validate them; a wild goose chase. This paper then uses this information to propose a new strategy that predictively and proactively selects valid candidates anywhere during the distance computation process without vetting every candidate. Theoretical analysis, simulations and experimental studies conducted led to a similar conclusion: 25% of the distance computations are wasteful and can be discarded without any repercussion on effectiveness.

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7

Wang, Rongcun, Zhengmin Li, Shujuan Jiang, and Chuanqi Tao. "Regression Test Case Prioritization Based on Fixed Size Candidate Set ART Algorithm." International Journal of Software Engineering and Knowledge Engineering 30, no. 03 (March 2020): 291–320. http://dx.doi.org/10.1142/s0218194020500138.

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Regression testing is a very time-consuming and expensive testing activity. Many test case prioritization techniques have been proposed to speed up regression testing. Previous studies show that no one technique is always best. Random strategy, as the simplest strategy, is not always so bad. Particularly, when a test suite has higher fault detection capability, the strategy can generate a better result. Nevertheless, due to the randomness, the strategy is not always as satisfactory as expected. In this context, we present a test case prioritization approach using fixed size candidate set adaptive random testing algorithm to reduce the effect of randomness and improve fault detection effectiveness. The distance between pair-wise test cases is assessed by exclusive OR. We designed and conducted empirical studies on eight C programs to validate the effectiveness of the proposed approach. The experimental results, confirmed by a statistical analysis, indicate that the approach we proposed is more effective than random and the total greedy prioritization techniques in terms of fault detection effectiveness. Although the presented approach has comparable fault detection effectiveness to ART-based and the additional greedy techniques, the time cost is much lower. Consequently, the proposed approach is much more cost-effective.

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Bloem, Roderick, Goerschwin Fey, Fabian Greif, Robert Könighofer, Ingo Pill, Heinz Riener, and Franz Röck. "Synthesizing adaptive test strategies from temporal logic specifications." Formal Methods in System Design 55, no. 2 (October 14, 2019): 103–35. http://dx.doi.org/10.1007/s10703-019-00338-9.

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Abstract Constructing good test cases is difficult and time-consuming, especially if the system under test is still under development and its exact behavior is not yet fixed. We propose a new approach to compute test strategies for reactive systems from a given temporal logic specification using formal methods. The computed strategies are guaranteed to reveal certain simple faults in every realization of the specification and for every behavior of the uncontrollable part of the system’s environment. The proposed approach supports different assumptions on occurrences of faults (ranging from a single transient fault to a persistent fault) and by default aims at unveiling the weakest one. We argue that such tests are also sensitive for more complex bugs. Since the specification may not define the system behavior completely, we use reactive synthesis algorithms with partial information. The computed strategies are adaptive test strategies that react to behavior at runtime. We work out the underlying theory of adaptive test strategy synthesis and present experiments for a safety-critical component of a real-world satellite system. We demonstrate that our approach can be applied to industrial specifications and that the synthesized test strategies are capable of detecting bugs that are hard to detect with random testing.

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Narciso, Everton Note, Márcio Eduardo Delamaro, and Fátima De Lourdes Dos Santos Nunes. "Test Case Selection: A Systematic Literature Review." International Journal of Software Engineering and Knowledge Engineering 24, no. 04 (May 2014): 653–76. http://dx.doi.org/10.1142/s0218194014500259.

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Time and resource constraints should be taken into account in software testing activities, and thus optimizing the test suite is fundamental in the development process. In this context, the test case selection aims to eliminate redundant or unnecessary test data, which is crucial for the definition of test strategies. This paper presents a systematic review on the test case selection conducted through a selection of 449 articles published in leading journals and conferences in Computer Science. We addressed the state-of-art by collecting and comparing existing evidence on the methods used in the different software domains and the methods used to evaluate the test case selection. Our study identified 32 papers that met the research objectives, which featured 18 different selection methods and were evaluated through 71 case studies. The most commonly reported methods are adaptive random testing, genetic algorithms and greedy algorithm. Most approaches rely on heuristics, such as diversity of test cases and code or model coverage. This paper also discusses the key concepts and approaches, areas of application and evaluation metrics inherent to the methods of test case selection available in the literature.

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10

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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11

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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Bai, Xiangqi, Liang Ma, and Lin Wan. "Statistical test of structured continuous trees based on discordance matrix." Bioinformatics 35, no. 23 (May 22, 2019): 4962–70. http://dx.doi.org/10.1093/bioinformatics/btz425.

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Abstract Motivation Cell fate determination is a continuous process in which one cell type diversifies to other cell types following a hierarchical path. Advancements in single-cell technologies provide the opportunity to reveal the continuum of cell progression which forms a structured continuous tree (SCTree). Computational algorithms, which are usually based on a priori assumptions on the hidden structures, have previously been proposed as a means of recovering pseudo trajectory along cell differentiation process. However, there still lack of statistical framework on the assessments of intrinsic structure embedded in high-dimensional gene expression profile. Inherit noise and cell-to-cell variation underlie the single-cell data, however, pose grand challenges to testing even basic structures, such as linear versus bifurcation. Results In this study, we propose an adaptive statistical framework, termed SCTree, to test the intrinsic structure of a high-dimensional single-cell dataset. SCTree test is conducted based on the tools derived from metric geometry and random matrix theory. In brief, by extending the Gromov–Farris transform and utilizing semicircular law, we formulate the continuous tree structure testing problem into a signal matrix detection problem. We show that the SCTree test is most powerful when the signal-to-noise ratio exceeds a moderate value. We also demonstrate that SCTree is able to robustly detect linear, single and multiple branching events with simulated datasets and real scRNA-seq datasets. Overall, the SCTree test provides a unified statistical assessment of the significance of the hidden structure of single-cell data. Availability and implementation SCTree software is available at https://github.com/XQBai/SCTree-test. Supplementary information Supplementary data are available at Bioinformatics online.

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13

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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15

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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Mao, Chengying, Xuzheng Zhan, Jinfu Chen, Jifu Chen, and Rubing Huang. "Adaptive random testing based on flexible partitioning." IET Software 14, no. 5 (October 1, 2020): 493–505. http://dx.doi.org/10.1049/iet-sen.2019.0325.

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17

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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18

Huang, Rubing, Jinfu Chen, and Yansheng Lu. "Adaptive Random Testing with Combinatorial Input Domain." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/843248.

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Random testing (RT) is a fundamental testing technique to assess software reliability, by simply selecting test cases in a random manner from the whole input domain. As an enhancement of RT, adaptive random testing (ART) has better failure‐detection capability and has been widely applied in different scenarios, such as numerical programs, some object‐oriented programs, and mobile applications. However, not much work has been done on the effectiveness of ART for the programs with combinatorial input domain (i.e., the set of categorical data). To extend the ideas to the testing for combinatorial input domain, we have adopted different similarity measures that are widely used for categorical data in data mining and have proposed two similarity measures based on interaction coverage. Then, we propose a new version named ART‐CID as an extension of ART in combinatorial input domain, which selects an element from categorical data as the next test case such that it has the lowest similarity against already generated test cases. Experimental results show that ART‐CID generally performs better than RT, with respect to different evaluation metrics.

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19

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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20

Yan, Min, Li Wang, and Aiguo Fei. "ARTDL: Adaptive Random Testing for Deep Learning Systems." IEEE Access 8 (2020): 3055–64. http://dx.doi.org/10.1109/access.2019.2962695.

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Chen, Tsong Yueh, Fei-Ching Kuo, and Huai Liu. "Distributing test cases more evenly in adaptive random testing." Journal of Systems and Software 81, no. 12 (December 2008): 2146–62. http://dx.doi.org/10.1016/j.jss.2008.03.062.

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22

Sen, Koushik. "Race directed random testing of concurrent programs." ACM SIGPLAN Notices 43, no. 6 (May 30, 2008): 11–21. http://dx.doi.org/10.1145/1379022.1375584.

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23

Klein, Casey, Matthew Flatt, and Robert Bruce Findler. "Random testing for higher-order, stateful programs." ACM SIGPLAN Notices 45, no. 10 (October 17, 2010): 555–66. http://dx.doi.org/10.1145/1932682.1869505.

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Chen, Jinfu, Patrick Kwaku Kudjo, Zufa Zhang, Chenfei Su, Yuchi Guo, Rubing Huang, and Heping Song. "A Modified Similarity Metric for Unit Testing of Object-Oriented Software Based on Adaptive Random Testing." International Journal of Software Engineering and Knowledge Engineering 29, no. 04 (April 2019): 577–606. http://dx.doi.org/10.1142/s0218194019500244.

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Finding an effective method for testing object-oriented software (OOS) has proven elusive in the software community due to the rapid development of object-oriented programming (OOP) technology. Although significant progress has been made by previous studies, challenges still exist in relation to the object distance measurement of OOS using Adaptive Random Testing (ART). This is partly due to the unique features of OOS such as encapsulation, inheritance and polymorphism. In a previous work, we proposed a new similarity metric called the Object and Method Invocation Sequence Similarity (OMISS) metric to facilitate multi-class level testing using ART. In this paper, we broaden the set of models in the metric (OMISS) by considering the method parameter and adding the weight in the metric to develop a new distance metric to improve unit testing of OOS. We used the new distance metric to calculate the distance between the set of objects and the distance between the method sequences of the test cases. Additionally, we integrate the new metric in unit testing with ART and applied it to six open source subject programs. The experimental result shows that the proposed method with method parameter considered in this study is better than previous methods without the method parameter in the case of the single method. Our finding further shows that the proposed unit testing approach is a promising direction for assisting software engineers who seek to improve the failure-detection effectiveness of OOS testing.

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Li, Zhibo, Qingbao Li, and Lei Yu. "An Enhanced Adaptive Random Testing by Dividing Dimensions Independently." Mathematical Problems in Engineering 2019 (October 13, 2019): 1–15. http://dx.doi.org/10.1155/2019/9381728.

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Random testing (RT) is widely applied in the area of software testing due to its advantages such as simplicity, unbiasedness, and easy implementation. Adaptive random testing (ART) enhances RT. It improves the effectiveness of RT by distributing test cases as evenly as possible. Fixed Size Candidate Set (FSCS) is one of the most well-known ART algorithms. Its high failure-detection effectiveness only shows at low failure rates in low-dimensional spaces. In order to solve this problem, the boundary effect of the test case distribution is analyzed, and the FSCS algorithm of a limited candidate set (LCS-FSCS) is proposed. By utilizing the information gathered from success test cases (no failure-causing test inputs), a tabu generation domain of candidate test case is produced. This tabu generation domain is eliminated from the current candidate test case generation domain. Finally, the number of test cases at the boundary is reduced by constraining the candidate test case generation domain. The boundary effect is effectively relieved, and the distribution of test cases is more even. The results of the simulation experiment show that the failure-detection effectiveness of LCS-FSCS is significantly improved in high-dimensional spaces. Meanwhile, the failure-detection effectiveness is also improved for high failure rates and the gap of failure-detection effectiveness between different failure rates is narrowed. The results of an experiment conducted on some real-life programs show that LCS-FSCS is less effective than FSCS only when the failure distribution is concentrated on the boundary. In general, the effectiveness of LCS-FSCS is higher than that of FSCS.

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Ackah-Arthur, Hilary, Jinfu Chen, Jiaxiang Xi, Michael Omari, Heping Song, and Rubing Huang. "A cost‐effective adaptive random testing approach by dynamic restriction." IET Software 12, no. 6 (December 2018): 489–97. http://dx.doi.org/10.1049/iet-sen.2017.0208.

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Selay, Elmin, Zhi Quan Zhou, Tsong Yueh Chen, and Fei-Ching Kuo. "Adaptive Random Testing in Detecting Layout Faults of Web Applications." International Journal of Software Engineering and Knowledge Engineering 28, no. 10 (September 25, 2018): 1399–428. http://dx.doi.org/10.1142/s0218194018500407.

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As part of a software testing process, output verification poses a challenge when the output is not numeric or textual, such as graphical. The industry practice of using human oracles (testers) to observe and verify the correctness of the actual results is both expensive and error-prone. In particular, this practice is usually unsustainable when developing web applications — the most popular software of our era. This is because web applications change frequently due to the fast-evolving requirements amid popular demand. To improve the cost effectiveness of browser output verification, in this study we design failure-based testing techniques and evaluate the effectiveness and efficiency thereof in the context of web testing. With a novel application of the concept of adaptive random sequence (ARS), our approach leverages peculiar characteristics of failure patterns found in browser layout rendering. An empirical study shows that the use of failure patterns and inclination to guide the testing flow leads to more cost-effective results than other classic methods. This study extends the application of ARSs from the input space of programs to their output space, and also shows that adaptive random testing (ART) can outperform random testing (RT) in both failure detection effectiveness (in terms of F-measure) and failure detection efficiency (in terms of execution time).

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Prasetyo, Sri Yulianto Joko, Kristoko Dwi Hartomo, and Mila Chrismawati Paseleng. "Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia." PeerJ Computer Science 7 (May 18, 2021): e415. http://dx.doi.org/10.7717/peerj-cs.415.

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This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

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Bubeck, Sébastien, Jian Ding, Ronen Eldan, and Miklós Z. Rácz. "Testing for high-dimensional geometry in random graphs." Random Structures & Algorithms 49, no. 3 (January 6, 2016): 503–32. http://dx.doi.org/10.1002/rsa.20633.

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Sinaga, Arnaldo Marulitua. "Adaptive Random Testing with Coverage Information for Object Oriented Program." Advanced Science Letters 23, no. 5 (May 1, 2017): 4359–62. http://dx.doi.org/10.1166/asl.2017.8338.

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Monemi Bidgoli, Atieh, and Hassan Haghighi. "Augmenting ant colony optimization with adaptive random testing to cover prime paths." Journal of Systems and Software 161 (March 2020): 110495. http://dx.doi.org/10.1016/j.jss.2019.110495.

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CHEN, Tsong-Yueh. "Impact of the Compactness of Failure Regions on the Performance of Adaptive Random Testing." Journal of Software 17, no. 12 (2006): 2438. http://dx.doi.org/10.1360/jos172438.

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Rao, Dr K. Koteswara, Saroja Mrs Y, Mr N. Ramesh Babu, Dr G. Lalitha Kumari, and Surekha Mrs Y. "Adaptive Genetic Algorithm (AGA) Based Optimal Directed Random Testing for Reducing Interactive Faults." Indian Journal of Computer Science and Engineering 12, no. 2 (April 20, 2021): 485–98. http://dx.doi.org/10.21817/indjcse/2021/v12i2/211202170.

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Omari, Michael, Jinfu Chen, Hilary Ackah-Arthur, and Patrick Kwaku Kudjo. "Elimination by Linear Association: An Effective and Efficient Static Mirror Adaptive Random Testing." IEEE Access 7 (2019): 71038–60. http://dx.doi.org/10.1109/access.2019.2919160.

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Pei, Hanyu, Beibei Yin, Min Xie, and Kai-Yuan Cai. "Dynamic random testing with test case clustering and distance-based parameter adjustment." Information and Software Technology 131 (March 2021): 106470. http://dx.doi.org/10.1016/j.infsof.2020.106470.

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Kuo, Fei-Ching, Tsong Yueh Chen, Huai Liu, and Wing Kwong Chan. "Enhancing adaptive random testing for programs with high dimensional input domains or failure-unrelated parameters." Software Quality Journal 16, no. 3 (March 8, 2008): 303–27. http://dx.doi.org/10.1007/s11219-008-9047-6.

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Chen, Jinfu, Qihao Bao, T. H. Tse, Tsong Yueh Chen, Jiaxiang Xi, Chengying Mao, Minjie Yu, and Rubing Huang. "Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion." IEEE Access 8 (2020): 52475–88. http://dx.doi.org/10.1109/access.2020.2977777.

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Sergey, Ilya. "Experience report: growing and shrinking polygons for random testing of computational geometry algorithms." ACM SIGPLAN Notices 51, no. 9 (December 5, 2016): 193–99. http://dx.doi.org/10.1145/3022670.2951927.

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Ramos-Guajardo, Ana Belén, Gil González-Rodríguez, and Ana Colubi. "Testing the degree of overlap for the expected value of random intervals." International Journal of Approximate Reasoning 119 (April 2020): 1–19. http://dx.doi.org/10.1016/j.ijar.2019.12.012.

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Del Carmen Pardo, Maria. "On testing independence in multidimensional contingency tables with stratified random sampling." Information Sciences 78, no. 1-2 (May 1994): 101–18. http://dx.doi.org/10.1016/0020-0255(94)90022-1.

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Zong, Cheng, Yanliang Zhang, Yongming Yao, Shiyong Shuang, and Ziyuan Wang. "A Comparison of Fault Detection Efficiency Between Adaptive Random Testing and Greedy Combinatorial Testing for Control Logics in Nuclear Industrial Distributed Control Systems." IEEE Access 9 (2021): 84021–33. http://dx.doi.org/10.1109/access.2021.3087165.

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Nguyen, Duc-Anh, Tran Nguyen Huong, Hieu Vo Dinh, and Pham Ngoc Hung. "Improvements of Directed Automated Random Testing in Test Data Generation for C++ Projects." International Journal of Software Engineering and Knowledge Engineering 29, no. 09 (September 2019): 1279–312. http://dx.doi.org/10.1142/s0218194019500402.

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This paper improves the breadth-first search strategy in directed automated random testing (DART) to generate a fewer number of test data while gaining higher branch coverage, namely Static DART or SDART for short. In addition, the paper extends the test data compilation mechanism in DART, which currently only supports the projects written in C, to generate test data for C++ projects. The main idea of SDART is when it is less likely to increase code coverage with the current path selection strategies, the static test data generation will be applied with the expectation that more branches are covered earlier. Furthermore, in order to extend the test data compilation of DART for C++ context, the paper suggests a general test driver technique for C++ which supports various types of parameters including basic types, arrays, pointers, and derived types. Currently, an experimental tool has been implemented based on the proposal in order to demonstrate its efficacy in practice. The results have shown that SDART achieves higher branch coverage with a fewer number of test data in comparison with that of DART in practice.

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Kim, HyungGyoon, Hyungmin Cho, and Changwoo Pyo. "GPU-based acceleration of the Linear Complexity Test for random number generator testing." Journal of Parallel and Distributed Computing 128 (June 2019): 115–25. http://dx.doi.org/10.1016/j.jpdc.2019.01.011.

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Iavich, Maksim, Tamari Kuchukhidze, Sergiy Gnatyuk, and Andriy Fesenko. "Novel Certification Method for Quantum Random Number Generators." International Journal of Computer Network and Information Security 13, no. 3 (June 8, 2021): 28–38. http://dx.doi.org/10.5815/ijcnis.2021.03.03.

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Random numbers have many uses, but finding true randomness is incredibly difficult. Therefore, quantum mechanics is used, using the essentially unpredictable behavior of a photon, to generate truly random numbers that form the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the performance of our device and the quality of the random bits generated. Self-testing as well as device independent quantum random number generation methods are analyzed in the paper. The advantages and disadvantages of both methods are identified. The model of a novel semi self-testing certification method for quantum random number generators is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. The method is very important for computer science, because it combines the best features from selftesting and device independent methods. It can be used, when the random numbers’ entropy depends on the device and when it does not. In the related researches, these approaches are offered to be used separately, depending on the random number generator. The offered novel certification technology can be properly used, when the device is compromised or spoiled. The technology can successfully detect unintended irregularities, operational problems, abnormalities and problems in the randomization process. The offered mythology assists to eliminate problems related to physical devices. The offered system has the higher certification randomness security and is faster than self-testing approaches. The method is rather efficient because it implements the different certification approaches in the parallel threads. The offered techniques make the offered research must more efficient than the other existing approaches. The corresponding programming simulation is implemented by means of the simulation techniques.

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Li, Jin-Fu, Yu-Jen Huang, and Yong-Jyun Hu. "Testing Random Defect and Process Variation Induced Comparison Faults of TCAMs With Asymmetric Cells." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 29, no. 11 (November 2010): 1843–47. http://dx.doi.org/10.1109/tcad.2010.2072710.

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Ashfaq, Muhammad, Rubing Huang, Dave Towey, Michael Omari, Dmitry Yashunin, Patrick Kwaku Kudjo, and Tao Zhang. "SWFC-ART: A cost-effective approach for Fixed-Size-Candidate-Set Adaptive Random Testing through small world graphs." Journal of Systems and Software 180 (October 2021): 111008. http://dx.doi.org/10.1016/j.jss.2021.111008.

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Sun, Li Yuan, and Yan Mei Zhang. "The Research and Application of the Variant Fuzz Testing Framework for Log Based on the Structured Data." Applied Mechanics and Materials 602-605 (August 2014): 1749–52. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1749.

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Fuzz testing is a software testing technique,which provides invalid, unexpected, or random data to the inputs of a computer program to test the robustness and security of procedures[1]. For structured data like logging, the variant fuzz testing framework adopts a configuration file, apply traverse and stream processing to complete the structured fuzzing. This article starts with the features of the structured data, then introduces the design and implementation of the variant fuzz testing framework, including function modules, class structure, and logic processing. As a conclusion, this framework is compared with zzuf tool, and the advanced nature of this framework is elaborated.

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CHANG, KAI H., JAMES H. CROSS II, W. HOMER CARLISLE, and SHIH-SUNG LIAO. "A PERFORMANCE EVALUATION OF HEURISTICS-BASED TEST CASE GENERATION METHODS FOR SOFTWARE BRANCH COVERAGE." International Journal of Software Engineering and Knowledge Engineering 06, no. 04 (December 1996): 585–608. http://dx.doi.org/10.1142/s0218194096000247.

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Software testing is an important step in the development of complex systems. The construction of test cases using traditional methods usually requires considerable manual effort. QUEST/Ada—Query Utility Environment for Software Testing of Ada, is a prototype test case generation system that uses various heuristics-based approaches to generate test cases. The system, which is designed for unit testing, generates test cases by monitoring the branch coverage progress and intelligently modifying existing test cases to achieve additional coverage. Three heuristics-based approaches along with a random test case generation method were studied to compare their branch coverage performance. Although some constraints are imposed by the prototype, the framework provides a useful foundation for further heuristics-based test case generation research. The design of the system, the heuristic rules used in the system, and an evaluation of each rule’s performance are presented.

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Rouillard, V., and G. T. Lleonart. "A Spectral Feedback Compensation Technique for the Generation of Random Wave Fields." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 208, no. 2 (March 1994): 105–12. http://dx.doi.org/10.1243/pime_proc_1994_208_106_02.

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Computer software was developed to control an electromechanical wave generator to accurately simulate random wave fields derived from mathematical spectral models. The software algorithm makes use of a spectral feedback control technique to improve accuracy and reliability in the continuous generation of random waves in the laboratory. Experiments were conducted to investigate the advantages of this closed-loop control technique over more commonly used open-loop random wave generation methods. The results show a decided advantage in using closed-loop control for the generation of random waves, especially for double-peak wave spectra and model testing purposes.

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Nayak, Tapan K. "Minimum Variance Unbiased Estimation of Software Reliability." Probability in the Engineering and Informational Sciences 3, no. 3 (July 1989): 335–40. http://dx.doi.org/10.1017/s0269964800001200.

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As the formal methods of proving correctness of a computer program are still very inadequate, in practice when a new piece of software is developed and all obvious errors are removed, it is tested with different (random) inputs in order to detect the remaining errors and assess its quality. We suppose that whenever the program fails the error causing the failure can be detected and removed correctly. Thus, the quality of the software increases as testing goes on. In this paper, we consider two different models and present the minimum variance unbiased estimators of the expected failure rate of the revised software at any time of testing t, based on the data generated up to that point.

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