Arrays for combinatorial interaction testing: a review on constructive approaches

Lu, Xiao-Nan; Jimbo, Masakazu

doi:10.1007/s42081-019-00056-w

Cited by 6 publications

(2 citation statements)

References 67 publications

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“…They analyzed the mathematical properties of these arrays. As [3], most of the studies on DAs and LAs focus on their mathematical aspects [16,17,18,19,20,21]. The application to screening experiments for TCP throughput in a mobile wireless network was reported in [22,23].…”

Section: Related Workmentioning

confidence: 99%

Constrained detecting arrays for fault localization in combinatorial testing

Jin

Shi

Tsuchiya

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Detecting Arrays (DAs) are mathematical objects that enable fault localization in combinatorial interaction testing. Each row of a DA serves as a test case, whereas a whole DA is treated as a test suite. In real-world testing problems, it is often the case that some constraints exist among test parameters. In this paper, we show that it may be impossible to construct a DA using only constraintsatisfying test cases. The reason for this is that a set of some faulty interactions may always mask the effect of other faulty interactions in the presence of constraints. Based on this observation, we propose the notion of Constrained Detecting Arrays (CDAs) to adapt DAs to practical situations. The definition of CDAs requires that all rows of a CDA must satisfy the constraints and the same fault localization capability as the DA must hold except for such inherently undetectable faults. We then propose a computational method for constructing CDAs. Experimental results obtained by using a program that implements the method show that the method was able to produce CDAs within a reasonable time for practical problem instances. CCS CONCEPTS • Software and its engineering → Software testing and debugging; • Mathematics of computing → Combinatorial optimization.

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Section: Related Workmentioning

confidence: 99%

Constrained detecting arrays for fault localization in combinatorial testing

Jin

Shi

Tsuchiya

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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“…Furthermore, the article includes a short review of a relationship between Euclidean designs for rotationally symmetric integrals and kernel approximation in machine learning. - Lu and Jimbo (2019) present a review of the brief history, basic problems, and significant results for constructing the arrays for combinatorial interaction testing. They also propose explicit construction for covering arrays involving information-theoretic methods.…”

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confidence: 99%

Special feature: information theory and statistics

Kanamori

2019

Jpn J Stat Data Sci

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In commemoration of the foundation of the Japanese Journal of Statistics and Data Science (JJSD), our third special feature focuses on relationships and collaborations between information theory and statistics. Development and expansion in research areas of information theory and statistics are drawing a great deal of attention. This special feature comprises ten contributions from mainly three research topics: divergence-based statistical inference, high-dimensional sparse learning, and combinatorial design.Divergence measure, an extension of distances over the set of probability distributions, is a transverse tool in mathematical sciences. In particular, the Kullback-Leibler divergence, or in other words, the relative entropy, is closely related to the maximum likelihood estimator in statistics and the code length in information theory (Kullback 1959;Cover and Thomas 2006). The information criterion (Akaike 1974) and Bayes coding (Clarke and Barron 2006) are interdisciplinary research topics based on the concept of divergences. Today, important classes of divergence measures such as Bregman divergences have been widely applied in data analysis and information sciences (Bregman 1967;Basu et al. 1998;Fujisawa and Eguchi 2008). The following five articles focus mainly on theoretical analysis and practical applications of divergence measures.- Machida and Takenouchi (2019) are concerned with non-negative matrix factorization (NMF), which is a typical means of feature extraction in the framework of unsupervised learning (Lee and Seung 2001). It is well known that the standard NMF algorithm is not robust against outlier noise. The authors propose robust NMF algorithms by combining statistical modeling of reconstruction and the -divergence.

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