On Favourable Conditions for Adaptive Random Testing

Chen, Tsong Yueh; Kuo, Fei-Ching; Zhi, Zhou

doi:10.1142/s0218194007003501

Cited by 38 publications

(75 citation statements)

References 13 publications

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“…It should be noted that the above setting is similar to the experimental setting of Experiment 3 in the study of Chen et al (2007d). Figure 16 shows the simulation results.…”

Section: Methodsmentioning

confidence: 97%

“…It should be noted that the setting of this experiment is exactly the same as Experiment 1 in the study of Chen et al (2007d). The results of these simulations are reported in Figure 14, which also includes the previous simulation results of FSCS-ART and RRT for ease of comparison.…”

Section: Dmentioning

confidence: 96%

“…As it has been reported by Chen et al (2007d) that F ART depends on many factors (such as θ, the dimension of D, the compactness and the number of failure regions, the existence and the size of a predominant failure region), theoretical study of F ART is extremely difficult. Hence, previous studies (Chen et al, 2004b(Chen et al, ,c, 2007cMayer, 2005;Chan et al, 2006) investigated F ART through either simulations or empirical studies.…”

Section: Notation and Conceptsmentioning

confidence: 99%

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Distributing test cases more evenly in adaptive random testing

Chen

Kuo

Liu

2008

Journal of Systems and Software

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Adaptive Random Testing (ART) has recently been proposed to enhance the failure-detection capability of Random Testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some * A preliminary version of this paper was presented at the 18th Australian Software Engineering Conference (ASWEC 2007) (Chen et al., 2007b

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“…It should be noted that the above setting is similar to the experimental setting of Experiment 3 in the study of Chen et al (2007d). Figure 16 shows the simulation results.…”

Section: Methodsmentioning

confidence: 97%

Section: Dmentioning

confidence: 96%

Section: Notation and Conceptsmentioning

confidence: 99%

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Distributing test cases more evenly in adaptive random testing

Chen

Kuo

Liu

2008

Journal of Systems and Software

Self Cite

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show abstract

“…Such a process was repeated for a sufficient number of times until the mean value of F-counts could be regarded as a reliable approximation for F ART within 95% confidence level and ±5% accuracy range (details on how to get the reliable approximation can be found in the study of Chen et al (2004)). Chen et al (2007c) have conducted an experiment to evaluate the fail-ure detection capability of ART when failure-causing inputs cluster together.…”

Section: One Test Case Selection Criterion Of Adaptive Random Testingmentioning

confidence: 99%

Adaptive random testing based on distribution metrics

Chen

Kuo

Liu

2009

Journal of Systems and Software

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Random testing (RT) is a fundamental software testing technique.Adaptive random testing (ART), an enhancement of RT, generally uses fewer test cases than RT to detect the first failure. ART generates test cases in a random manner, together with additional test case selection criteria to enforce that the executed test cases are evenly spread over the input domain. Some studies have been conducted to measure how evenly an ART algorithm can spread its test cases with respect to some distribution metrics. These studies observed that there exists a correlation between the failure detection capability and the evenness of test case distribution. Inspired by this observation, we aim to study whether failure detection capability of ART can be * A preliminary version of this paper was presented at the 7th International Conference on Quality Software (QSIC 2007) (Chen et al., 2007a

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“…More recently, another testing strategy named Adaptive Random Testing (ART) has been proposed to improve the fault-detection capability of random testing without the above limitations [6], [7], [8], [9]. ART is based on the intuition that when failure-causing inputs are clustered, selecting an input close to the previously executed test cases that have not revealed a failure is less likely to detect a failure.…”

Section: Introductionmentioning

confidence: 99%

Improving Software Testing Cost-Effectiveness through Dynamic Partitioning

Zhi

Sinaga

Zhao

et al. 2009

2009 Ninth International Conference on Quality Software

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We present a dynamic partitioning strategy that selects test cases using online feedback information. The presented strategy differs from conventional approaches. Firstly, the partitioning is carried out online rather than off-line. Secondly, the partitioning is not based on program code or specifications; instead, it is simply based on the fail or pass information of previously executed test cases and, hence, can be implemented in the absence of the source code or specification of the program under test. The cost-effectiveness of the proposed strategy has been empirically investigated with three programs, namely SPACE, SED, and GREP. The results show that the proposed strategy achieves a significant saving in terms of total number of test cases executed to detect all faults. Abstract-We present a dynamic partitioning strategy that selects test cases using online feedback information. The presented strategy differs from conventional approaches. Firstly, the partitioning is carried out online rather than off-line. Secondly, the partitioning is not based on program code or specifications; instead, it is simply based on the fail or pass information of previously executed test cases and, hence, can be implemented in the absence of the source code or specification of the program under test. The cost-effectiveness of the proposed strategy has been empirically investigated with three programs, namely SPACE, SED, and GREP. The results show that the proposed strategy achieves a significant saving in terms of total number of test cases executed to detect all faults. Disciplines Physical Sciences and Mathematics

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On Favourable Conditions for Adaptive Random Testing

Cited by 38 publications

References 13 publications

Distributing test cases more evenly in adaptive random testing

Distributing test cases more evenly in adaptive random testing

Adaptive random testing based on distribution metrics

Improving Software Testing Cost-Effectiveness through Dynamic Partitioning

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