• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.19-28
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• 2011

Adaptive Random Testing (ART) aims to enhance the performance of pure random testing by detecting failure region in a software. The ART algorithm generates effective test cases which requires less number of test cases than that of pure random testing. However, all ART algorithms currently proposed are designed for the tests of monolithic system or unit level. In case of integrated system tests, ART approaches do not achieve same performances as those of ARTs applied to the unit or monolithic system. In this paper, we propose an extended ART algorithm which can be applied to the integrated system testing environment without degradation of performance. The proposed approach investigates an input distribution of the unit under a test with limited number of seed input data and generates information to be used to resizing input domain partitions. The simulation results show that our approach in an integration environment could achieve similar level of performance as an ART is applied to a unit testing. Results also show resilient effectiveness for various failure rates.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.531-540
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• 2008

An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.180-191
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• 2008

An Adaptive Random Testing (ART) is one of test case generation algorithms that are designed to detect common failure patterns within input domain. The ART algorithm shows better performance than that of pure Random Testing (RT). Distance-bases ART (D-ART) and Restriction Random Testing (RRT) are well known examples of ART algorithms which are reported to have good performances. But significant drawbacks are observed as quadratic runtime and non-uniform distribution of test case. They are mainly caused by a huge amount of distance computations to generate test case which are distance based method. ART through Iterative Partitioning (IP-ART) significantly reduces the amount of computation of D-ART and RRT with iterative partitioning of input domain. However, non-uniform distribution of test case still exists, which play a role of obstacle to develop a scalable algerian. In this paper we propose a new ART method which mitigates the drawback of IP-ART while achieving improved fault-detection capability. Simulation results show that the proposed one has about 9 percent of improved F-measures with respect to other algorithms.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.451-461
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• 2009

The Adaptive Random Testing(ART) aims to improve the performance of traditional Random Testing(RT) by reducing the number of test cases to find the failure region which is located in the input domain. Such enhancement can be obtained by efficient selection algorithms of test cases. The ART through Iterative Partitioning(IP-ART) is one of ART techniques and it uses an iterative input domain partitioning method to improve the performance of early-versions of ART which have significant drawbacks in computation time. And the IP-ART with Enlarged Input Domain(EIP-ART), an improved version of IP-ART, is known to make additional performance improvement with scalability by expanding to virtual test space beyond real input domain of IP-ART. The EIP-ART algorithm, however, have the drawback of heavy cost of computation time to generate test cases mainly due to the virtual input domain enlargement. For this reason, two algorithms are proposed in this paper to mitigate the computation overhead of the EIP-ART. In the experiments by simulations, the tiling technique of input domain, one of two proposed algorithms, showed significant improvements in terms of computation time and testing performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.64-76
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• 2009

Adaptive Random Testing through Iterative Partitioning(IP-ART) is one of Adaptive Random Testing(ART) techniques. IP-ART uses an iterative partitioning method for input domain to improve the performances of early-versions of ART that have significant drawbacks in computation time. Another version of IP-ART, named with EIP-ART(IP-ART with Enlarged Input Domain), uses virtually enlarged input domain to remove the unevenly distributed parts near the boundary of the domain. EIP-ART could mitigate non-uniform test case distribution of IP-ART and achieve relatively high performances in a variety of input domain environments. The EIP-ART algorithm, however, have the drawback of higher computation time to generate test cases mainly due to the additional workload from enlarged input domain. For this reason, a revised version of IP-ART without input domain enlargement needs to improve the distribution of test cases to remove the additional time cost. We explore three smoothing algorithms which influence the distribution of test cases, and analyze to check if any performance improvements take place by them. The simulation results show that the algorithm of a restriction area management achieves better performance than other ones.