A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

Chen, Jinfu; Kuo, Fei-Ching; Chen, Tsong Yueh; Towey, Dave; Su, Chenfei; Huang, Rubing

doi:10.1109/tr.2016.2628759

Cited by 25 publications

(16 citation statements)

References 39 publications

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“…Some earlier research studies have shown that the probability of achieving higher failure-detection is by evenly spreading test cases across the input domain [3][4][5][6]. RT, although has lower effectiveness, has almost a linear efficiency as its algorithm selects test cases randomly from the input domain without considering their even spread.…”

Section: Introductionmentioning

confidence: 99%

A cost‐effective adaptive random testing approach by dynamic restriction

Ackah-Arthur

Chen

et al. 2018

IET softw.

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A key objective of software testing is to find program errors that cause failure in software, at less cost. One basic testing technique is random testing (RT), but many researchers have criticised its failure-detection effectiveness. Several researchers have proposed that an enhancement of the failure-detection effectiveness of RT is achieved if test cases are evenly spread within the input domain. Adaptive RT (ART) describes a family of algorithms that employ various strategies to evenly and randomly spread test cases. Fixed sized candidate set ART (FSCS-ART) is an ART algorithm that has gained many research studies far and wide; however, the high distance computations make its algorithm computationally expensive. The authors propose a new ART method that restricts distance computations to only test cases inside an exclusion zone. The experimental results show that the new ART method not only improves RT but also provides failure-detection effectiveness similar to FSCS-ART, while significantly minimising computation overhead.

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Section: Introductionmentioning

confidence: 99%

A cost‐effective adaptive random testing approach by dynamic restriction

Ackah-Arthur

Chen

et al. 2018

IET softw.

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“…• Some studies found ART to be more cost-effective than RT (e.g., [114], [150], [166]), with some observing ART to require less time than RT to identify the first failure (e.g., [40], [144]).…”

Section: Summary Of Main Results For Application Domainsmentioning

confidence: 99%

“…Although many approaches have been proposed for ART, there are very few tools [40], [73], [114], [144], [150], some of which are: AutoTest, which supports ART for objectoriented (ARTOO) programs written in Eiffel [40]; ART-Gen, which supports divergence-oriented ART for Java programs [114]; Practical Extensions of Random Testing (PERT), which supports testing for various input types [150]; and OMISS-ART, which supports FSCS for C++ and C# programs. However, these tools are not publicly available.…”

Section: Challenge 6: Art Toolsmentioning

confidence: 99%

A Survey on Adaptive Random Testing

Huang

Sun

et al. 2021

IIEEE Trans. Software Eng.

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Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.

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“…For instance, a linear-time ARTsum algorithm was recommended by Barus et al [32] using the concepts of categories and choices in category-partition testing. Ciupa et al [8] and Chen et al [9] introduced the ARTOO and the OMISS metrics for modeling the distances between objects and between method innovation sequences. We have not compared RRT-LAZ with these algorithms experimentally because they cater for non-numerical inputs.…”

Section: Further Discussion On Related Workmentioning

confidence: 99%

Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion

et al. 2020

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Adaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the neighborhoods (or zones) of previously executed test cases. RRT has higher effectiveness than RT in terms of failure detection but incurs a higher time cost. In this paper, we aim to further reduce the time costs for RRT and improve the effectiveness for RT and ART methods. We propose a proactive technique known as ''RRT by largest available zone'' (RRT-LAZ). Like RRT, RRT-LAZ first defines an exclusion zone around every executed test case in order to determine the available zones. Unlike the original RRT, RRT-LAZ then compares all the available zones to proactively pick the largest one, from which the next test case is randomly generated. Both simulation analyses and empirical studies have been employed to investigate the efficiency and effectiveness of RRT-LAZ in relation to RT and related ART algorithms. The results show that RRT-LAZ has significantly lower time costs than RRT. Furthermore, RRT-LAZ is more effective than RT and related ART methods for block failure patterns in low-dimensional input spaces. In general, since RRT-LAZ employs a proactive technique instead of a passive one in generating next cases, it is much more cost-effective than RRT. RRT-LAZ is also more cost-effective than RT and other ART methods that we have studied. INDEX TERMS Software testing, random testing, adaptive random testing, restricted random testing, exclusion zone, largest available zone. I. BACKGROUND

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A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

Cited by 25 publications

References 39 publications

A cost‐effective adaptive random testing approach by dynamic restriction

A cost‐effective adaptive random testing approach by dynamic restriction

A Survey on Adaptive Random Testing

Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion

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