New bounds for mixed covering arrays in t-way testing with uniform strength

Gonzalez-Hernandez, Loreto

doi:10.1016/j.infsof.2014.10.009

Cited by 13 publications

(12 citation statements)

References 32 publications

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“…Hyper planes are contrasting option to metaeuristics is seen as an abnormal state technique that produce a abnormal solution set for the possible structure in vary of set which plays out a hunt over the space framed by a set of the mill meta-heuristics, there is a sensible partition between the issue area and the abnormal state hyper-heuristic [11]. Aside from expanding the level of consensus, hyper-heuristics can likewise be focused with bespoke metaheuristics.…”

Section: Related Workmentioning

confidence: 99%

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

Velagandula¹,

Reddy²,

Reddy³

et al. 2018

IJET

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Section: Related Workmentioning

confidence: 99%

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

Velagandula¹,

Reddy²,

Reddy³

et al. 2018

IJET

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“…Normally each function has a different number of features depending on the system-under-test. To this end, mixed covering array (MCA) is used as a practical alternative, which is defined as below [11]:…”

Section: Constrained Covering Array (Cca)mentioning

confidence: 99%

Handling constraints in combinatorial interaction testing in the presence of multi objective particle swarm and multithreading

Ahmed

Gambardella

Afzal

et al. 2017

Information and Software Technology

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Context: Combinatorial testing strategies have lately received a lot of attention as a result of their diverse applications. In its simple form, a combinatorial strategy can reduce several input parameters (configurations) of a system into a small set based on their interaction (or combination). In practice, the input configurations of software systems are subjected to constraints, especially in case of highly configurable systems. To implement this feature within a strategy, many difficulties arise for construction. While there are many combinatorial interaction testing strategies nowadays, few of them support constraints.Objective: This paper presents a new strategy, to construct combinatorial interaction test suites in the presence of constraints.Method: The design and algorithms are provided in detail. To overcome the multi-judgement criteria for an optimal solution, the multi-objective particle swarm optimisation and multithreading are used. The strategy and its associated algorithms are evaluated extensively using different benchmarks and comparisons.Results: Our results are promising as the evaluation results showed the efficiency and performance of each algorithm in the strategy. The benchmarking results also showed that the strategy can generate constrained test suites efficiently as compared to state-of-the-art strategies.Conclusion:The proposed strategy can form a new way for constructing of constrained combinatorial interaction test suites. The strategy can form a new and effective base for future implementations.of input parameters based on their combination. Each combination could form a specific configuration of the software-under-test (SUT). The goal is to cover all possible t-combinations (sometimes called t − tuples) by an optimised set (where t is the interaction strength) [1]. This could be a difficult task in case of highly configurable systems, which leads to combinatorial explosion and nondeterministic polynomial-time hard (NP-hard) problems [2]. In addition, the problem of constrained interactions has recently appeared [3].Nowadays, software development is shifted from building an isolated, product-by-product approach to software product lines (SPL) [4]. In addition to the many features this approach provides like minimising the cost and market reachability time, it facilitates the idea of customisable software products. With this approach, there are many customisable features that could be added to or extracted from the functionality of a specific software based on the needs of the developers or customers. Eclipse represents a well-known example of SPL in which many functions and plug-ins (i.e., having different features) can be added or extracted from its main framework [5]. Evidence showed that most of the faults may result due to the interaction among these features [1,6]. Hence, all the interactions must be tested carefully. However, in reality, there are many constraints among the features. Some features must or must not appear with others and they may be included or excluded from th...

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“…Usually, each function has a different number of features depending on the system-under-test. To this end, mixed covering array (MCA) is used as a practical alternative, which is defined as below [18]:…”

Section: Covering Array (Ca)mentioning

confidence: 99%

“…For more flexibility in the notation, the array can be presented by MCA (N;d, v k ) and can be used for a fixedlevel CA, such as CA (N;t, v k ) [18]. So far, these notations have been used within combinatorial testing literature.…”

Section: V2 … Vk)) Is An N × K Array On V Levels Where the Rows Ofmentioning

confidence: 99%

Generating Pairwise Combinatorial Interaction Test Suites Using Single Objective Dragonfly Optimisation Algorithm

Ahmed¹

2017

JZS

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Combinatorial interaction testing has been addressed as an effective software testing technique recently. It shows its ability to reduce the number of test cases that have to be considered for softwareunder-test by taking the combinations of parameters as an interaction of input. This combination could be considered as input-configuration of different software families. Pairwise combinatorial test suite takes the interaction of two input parameters into consideration instead of many parameter interactions. Evidence showed that this test suite could detect most of the faults in the software-under-test as compared to higher interactions. This paper presents a new technique to generate pairwise combinatorial test suites. Also, Dragon Fly (DF), a new swarm intelligent optimization algorithm, is assessed. The design and adaptation of the algorithm are addresses in the paper in detail. The algorithm is evaluated extensively through different experiments and benchmarks. The evaluation shows the efficiency of the proposed technique for test suite generation and the usefulness of DF optimization algorithm for future investigations.

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New bounds for mixed covering arrays in t-way testing with uniform strength

Cited by 13 publications

References 32 publications

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

Handling constraints in combinatorial interaction testing in the presence of multi objective particle swarm and multithreading

Generating Pairwise Combinatorial Interaction Test Suites Using Single Objective Dragonfly Optimisation Algorithm

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