Mirror adaptive random testing

Chen, Tsong Yueh; Kuo, Fei-Ching; Merkel, Robert; Ng, Sebastian

doi:10.1016/j.infsof.2004.07.004

Cited by 84 publications

(95 citation statements)

References 8 publications

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“…The ECP approach proposed in this paper is applicable for ART-IP and ART-RP. For LART and many other ART algorithms (such as Mirror ART (Chen et al, 2004b) and ART by Localization (Chen and Huang, 2004)), since they have more complicated test case distributions than FSCS-ART and RRT, further investigations are required to find appropriate partitioning schemes for them.…”

Section: Discussionmentioning

confidence: 99%

“…F-measure is more appropriate than other testing effectiveness metrics to measure ART/RT, as justified by Chen and Merkel (2008), so we will follow previous studies (Chen et al, 2004b(Chen et al, ,c, 2007cMayer, 2005;Chan et al, 2006) to use F-measure for evaluating the failure-detection capability of ART.…”

Section: Notation and Conceptsmentioning

confidence: 99%

“…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. For simulations, θ and the failure pattern were predefined.…”

Section: Notation and Conceptsmentioning

confidence: 99%

“…The simulation was repeated for a sufficient number (S) of times to ensure that F ART is accurate within a certain confidence level and a certain accuracy range (the details of calculating S can be found in the study of Chen et al (2004b)). In this paper, the default values of confidence level and accuracy range are set as 95% and ±5%, respectively.…”

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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Section: Discussionmentioning

confidence: 99%

Section: Notation and Conceptsmentioning

confidence: 99%

Section: Notation and Conceptsmentioning

confidence: 99%

Section: Notation and Conceptsmentioning

confidence: 99%

See 2 more Smart Citations

Distributing test cases more evenly in adaptive random testing

Chen

Kuo

Liu

2008

Journal of Systems and Software

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“…During the software development stage, the design defects will evolve to the coding defects, that is to say, when the defect code is executed, the software will defect [5] . In order to find the underlying defects, we must select the test suites belong to the software defect region.…”

Section: Input Constraintmentioning

confidence: 99%

Software Defect Regions Identification Based on Behavior Mining

Guo¹,

Wang²,

Wang³

2015

Advances in Computer Science Research

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Abstract. Random testing is a widely used testing technique, and adaptive random testing has enhanced the performance of random testing recently. The research results demonstrate that the improvement depends on the characters of the software defect region. A method that depicts the distribution of software defect region in the input region based on testing is proposed in this paper, and the method that describes the characteristics of the software defect region. The experimental results show that testing constraint reveals the mechanism of software defect trigging and propagation.

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Adaptive Random Testing Through Iterative Partitioning

Chen

Huang

Zhi

2006

Reliable Software Technologies – Ada-Europe 2006

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Random Testing (RT) is an important and fundamental approach to testing computer software. Adaptive Random Testing (ART) has been proposed to improve the faultdetection capability of RT. ART employs the location information of successful test cases (those that have been executed but not revealed a failure) to enforce an even spread of random test cases across the input domain. Distance-based ART (D-ART) and Restriction-based ART (R-ART) are the first two ART methods, which have considerably improved the fault-detection capability of RT. Both these methods, however, require additional computation to ensure the generation of evenly spread test cases. To reduce the overhead in test case generation, we present in this paper a new ART method using the notion of iterative partitioning. The input domain is divided into equally sized cells by a grid. The grid cells are categorized into three different groups according to their relative locations to successful test cases. In this way, our method can easily identify those grid cells that are far apart from all successful test cases for test case generation. Our method significantly reduces the time complexity, while keeping the high fault-detection capability.

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Mirror adaptive random testing

Cited by 84 publications

References 8 publications

Distributing test cases more evenly in adaptive random testing

Distributing test cases more evenly in adaptive random testing

Software Defect Regions Identification Based on Behavior Mining

Adaptive Random Testing Through Iterative Partitioning

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