On a Combinatorial Framework for Fault Characterization

Colbourn, Charles J.; Syrotiuk, Violet R.

doi:10.1007/s11786-018-0385-x

Cited by 19 publications

(10 citation statements)

References 53 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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“…Mainly, CIT used in software testing and program verification. There are many applications in this direction, for example, fault detection and characterisation [9, 10], graphical user interface (GUI) testing [11], model‐based testing, and mutation testing [12, 13]. There are many more applications of CIT in software testing.…”

Section: Basic Concepts and Literaturementioning

confidence: 99%

Code‐aware combinatorial interaction testing

et al. 2019

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Combinatorial interaction testing (CIT) is a useful testing technique to address the interaction of input parameters in software systems. In many applications, the technique has been used as a systematic sampling technique to sample the enormous possibilities of test cases. In the last decade, most of the research activities focused on the generation of CIT test suites as it is a computationally complex problem. Although promising, less effort has been paid for the application of CIT. In general, to apply the CIT, practitioners must identify the input parameters for the Software-under-test (SUT), feed these parameters to the CIT tool to generate the test suite, and then run

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“…Recently, techniques based on resampling [17] have been investigated for locating array construction. Studies in this line include [18,19].…”

Section: Related Workmentioning

confidence: 99%

Using simulated annealing for locating array construction

Konishi¹,

Kojima²,

Nakagawa³

et al. 2019

Preprint

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Context: Combinatorial interaction testing is known to be an efficient testing strategy for computing and information systems. Locating arrays are mathematical objects that are useful for this testing strategy, as they can be used as a test suite that enables fault localization as well as fault detection. In this application, each row of an array is used as an individual test.Objective: This paper proposes an algorithm for constructing locating arrays with a small number of rows. Testing cost increases as the number of tests increases; thus the problem of finding locating arrays of small sizes is of practical importance. Method:The proposed algorithm uses simulation annealing, a metaheuristic algorithm, to find locating array of a given size. The whole algorithm repeatedly executes the simulated annealing algorithm by dynamically varying the input array size.Results: Experimental results show 1) that the proposed algorithm is able to construct locating arrays for problem instances of large sizes and 2) that, for problem instances for which nontrivial locating arrays are known, the algorithm is often able to generate locating arrays that are smaller than or at least equal to the known arrays. Conclusion:Based on the results, it is concluded that the proposed algorithm can produce small locating arrays and scale to practical problems.

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On a Combinatorial Framework for Fault Characterization

Cited by 19 publications

References 53 publications

Constrained detecting arrays for fault localization in combinatorial testing

Constrained detecting arrays for fault localization in combinatorial testing

Code‐aware combinatorial interaction testing

Using simulated annealing for locating array construction

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