An elitist-flower pollination-based strategy for constructing sequence and sequence-less t-way test suite

Nasser, Abdullah B.; Zamli, Kamal Z.; Alsewari, AbdulRahman A.; Ahmed, Bestoun S.

doi:10.1504/ijbic.2018.094223

Cited by 15 publications

(14 citation statements)

References 13 publications

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“…The CAs are well studied in the literature ( 7), examples for one-test-at-a-time approach involve: Automatic Efficient Test Generator (AETG) (8), IBM's Intelligent Test Case Handler (ITCH) (9), Jenny (10), simulated annealing (SA) (8), genetic algorithm (GA) (9), ant colony algorithm (ACA) (10), Hill climbing (HC) (11), GTWAY (12), particle swarm optimization (PSO) (13), harmony search (HS) (14), cuckoo search (CS) (15), high level hyper-heuristic (HHH) ( 16), adaptive teaching learning-based optimization (ALTBO) (17), flower pollination algorithm (FPA) (18), elitist flower pollination algorithm (eFPA) (1). Examples for one-parameter-at-a-time involve: Input Parameter Order-Generalized (IPOG) (4, 5), the modified IPOG (MIPOG), IPOD, Forbes' IPOG (IPOG-F) (6), and OPAT-HS (19).…”

Section: Related Workmentioning

confidence: 99%

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DEO: A Dynamic Event Order Strategy for t-way Sequence Covering Array Test Data Generation

Younis

2020

Baghdad Sci.J

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Sequence covering array (SCA) generation is an active research area in recent years. Unlike the sequence-less covering arrays (CA), the order of sequence varies in the test case generation process. This paper reviews the state-of-the-art of the SCA strategies, earlier works reported that finding a minimal size of a test suite is considered as an NP-Hard problem. In addition, most of the existing strategies for SCA generation have a high order of complexity due to the generation of all combinatorial interactions by adopting one-test-at-a-time fashion. Reducing the complexity by adopting one-parameter- at-a-time for SCA generation is a challenging process. In addition, this reduction facilitates the supporting for a higher strength of coverage. Motivated by such challenge, this paper proposes a novel SCA strategy called Dynamic Event Order (DEO), in which the test case generation is done using one-parameter-at-a-time fashion. The details of the DEO are presented with a step-by-step example to demonstrate the behavior and show the correctness of the proposed strategy. In addition, this paper makes a comparison with existing computational strategies. The practical results demonstrate that the proposed DEO strategy outperforms the existing strategies in term of minimal test size in most cases. Moreover, the significance of the DEO increases as the number of sequences increases and/ or the strength of coverage increases. Furthermore, the proposed DEO strategy succeeds to generate SCAs up to t=7. Finally, the DEO strategy succeeds to find new upper bounds for SCA. In fact, the proposed strategy can act as a research vehicle for variants future implementation.

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Section: Related Workmentioning

confidence: 99%

“…Some of these strategies are based on algebraic algorithms in which the test size is near minimal (2,3). Nevertheless, search based computational strategies are also considered as competitive strategies and can produce minimal test suite for some configurations (1,3).…”

Section: Introductionmentioning

confidence: 99%

DEO: A Dynamic Event Order Strategy for t-way Sequence Covering Array Test Data Generation

Younis

2020

Baghdad Sci.J

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“…. , CA k )) represents N × p MCA of strength t containing vectors of CA 1 to CA k , and a subset of the k columns each of strength > t [7,8]. Also, practically, not all the inputs are interacting and having an impact on each other.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…Variable strength CA (VSCA) is introduced to cater for this issue. A

VSCA false(N; t; k, v, false(C {normalA}_{1}, . . ., C {normalA}_{k} false) false)

represents

N \times p

MCA of strength t containing vectors of

C {normalA}_{1}

C {normalA}_{k}

, and a subset of the k columns each of strength

> t

[7, 8]. Also, practically, not all the inputs are interacting and having an impact on each other.…”

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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“…These strategies can be either deterministic or non-deterministic , Cohen, et al, 1997;Lei, et al, 2008. Some of the combinatorial strategies adopted metaheuristics algorithms for searching the test space , Ahmed, et al 2012;Alsewari and Zamli, 2012;Hazli, et al, 2012;Rahman, et al, 2014;Yang, et al, 2014;Ahmed, et al, 2015;Prasad, et al, 2015;Zamli, et al, 2016;Rabbi, 2017;Zamli, et al, 2017;Nasser, et al, 2018. Due to the NP-Hard problem, Colburn makes a dedicated website to record minimal upper bound for CA ,Colbourn, 2019. Earlier works also reported that the number of tuples increased significantly when increased the number of parameters and/or the strength of coverage and/or the parameter-values.…”

Section: Research Trends In Combinatorial Testingmentioning

confidence: 99%

MVSCA: Multi-Valued Sequence Covering Array

Younis

2019

jcoeng

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This paper discusses the limitation of both Sequence Covering Array (SCA) and Covering Array (CA) for testing reactive system when the order of parameter-values is sensitive. In doing so, this paper proposes a new model to take the sequence values into consideration. Accordingly, by superimposing the CA onto SCA yields another type of combinatorial test suite termed Multi-Valued Sequence Covering Array (MVSCA) in a more generalized form. This superimposing is a challenging process due to NP-Hardness for both SCA and CA. Motivated by such a challenge, this paper presents the MVSCA with a working illustrative example to show the similarities and differences among combinatorial testing methods. Consequently, the MVSCA is a new trend that can be a research vehicle for researchers to develop new and/or modify existing combinatorial strategies to deal with the combinatorial explosion problem raised by the MVSCA.

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An elitist-flower pollination-based strategy for constructing sequence and sequence-less t-way test suite

Cited by 15 publications

References 13 publications

DEO: A Dynamic Event Order Strategy for t-way Sequence Covering Array Test Data Generation

DEO: A Dynamic Event Order Strategy for t-way Sequence Covering Array Test Data Generation

Code‐aware combinatorial interaction testing

MVSCA: Multi-Valued Sequence Covering Array

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