Assessing IRPS as an efficient pairwise test data generation strategy

Younis, Mohammed Issam; Zamli, Kamal Z.; Klaib, Mohammad F. J.; Soh, Zainal Hisham Che; Abdullah, Syahrul Afzal Che; Isa, Nor Ashidi Mat

doi:10.1504/ijaip.2010.029443

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…The effectiveness of AFSO was tested through the construction of pairwise covering arrays for non-prime-power orders v ∈ {10, 12,14,15,18,20,21,22, 24} and number of columns k ≤ 24. The results provided by AFSO were very competitive with the current best-known results.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, test suites for non‐prime‐power orders have been constructed using greedy and metaheuristic methods. In the class of greedy methods to construct covering arrays for non‐prime‐power orders, we can mention the building‐block algorithm [14], the intersection residual pair set strategy [15], coverage inheritance [16], in‐parameter‐order [17], and deterministic density algorithm [18]. On the other hand, metaheuristic methods include simulated annealing [19, 20] and tabu search [21].…”

Section: Introductionmentioning

confidence: 99%

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Improved pairwise test suites for non‐prime‐power orders

Ávila-George

Torres-Jiménez²,

Izquierdo-Marquez³

2018

IET softw.

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Software testing has become a critical component of the modern software development process. Therefore, a lot of research has been done in this area in recent years, and as a result new algorithms, methodologies, and tools have been created. One of the most used testing strategies is pairwise testing; this technique ensures that all possible combinations of values between any two input parameters are covered by at least one test. In this work, a new algorithm called add factor and stochastic optimisation (AFSO) is used to build small pairwise test suites for non-prime-power orders. Starting from an orthogonal array of order v ∈ {10, 12, 14, 15, 18, 20, 21, 22, 24}, AFSO iteratively adds a factor and then reduces to zero the number of uncovered combinations by means of a simulated annealing algorithm. The results of the AFSO algorithm improved the size of 92 pairwise test suites with non-prime-power orders. One of these improved test suites is used in a real-word application to show the usefulness of the new results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved pairwise test suites for non‐prime‐power orders

Ávila-George

Torres-Jiménez²,

Izquierdo-Marquez³

2018

IET softw.

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“…In one-test-at-a-time approach, CA is constructed one row at a time and the algorithms using this approach usually differ in the way rows are constructed. The well known strategies under this approach are Automatic Efficient Test Generator (AETG) [3], Test Case Generation (TCG) [30], Classification-Tree Editor eXtended Logics (CTE-XL) [31], Jenny [32], Pairwise Independent Combinatorial Testing (PICT) [33], Deterministic Density Algorithm (DDA) [34,35], Intersection Residual Pair Set Strategy (IRPS) [36]. In case of one-parameter-at-a-time approach, CA is generated for the first two parameters, and then it is extended to generate CA for the first three parameters, and continues to do so for each additional parameter [2,37].…”

Section: Related Workmentioning

confidence: 99%

Construction of Strength Two Mixed Covering Arrays Using Greedy Mutation in Genetic Algorithm

Sabharwal¹,

Bansa²,

Mittal³

2015

IJITCS

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Metaheuristic methods are capable of solving a wide range of combinatorial problems competently. Genetic algorithm (GA) is a metaheuristic search based optimization algorithm that can be used to generate optimal Covering Arrays (CAs) and Mixed Covering Arrays (MCAs) for pair-wise testing. Our focus in the work presented in this paper is on the strategies of performing mutation in GA to enhance the overall performance of GA in terms of solution quality and computational time (number of generations). This is achieved by applying a greedy approach to perform mutation at a position that minimizes the loss of existing distinct pairs in the parent CA/MCA and ensures that the generated offspring is of good quality. Experiments are conducted on several benchmark problems to evaluate the performance of the proposed greedy based GA with respect to the existing state-of-the-art algorithms. Our evaluation shows that the proposed algorithm outperforms its GA counterpart by generating better quality MCA in lesser number of generations. Also the proposed approach yields better/comparable results compared to the existing state-of-the-art algorithms for generating CAs and MCAs.

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“…Nonetheless, algebraic approaches often impose restrictions on the system configurations to which they can be applied [61] (with some fixed generalizations/assumptions). These restrictions significantly limit the applicability of algebraic approaches for software testing.…”

Section: Related Workmentioning

confidence: 99%

Design and implementation of a t-way test data generation strategy with automated execution tool support

Zamli

Klaib

Younis

et al. 2011

Information Sciences

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a b s t r a c tTo ensure an acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations. However, due to the combinatorial explosion problem, exhaustive testing is practically impossible. Resource constraints, cost factors, and strict time-to-market deadlines are some of the main factors that inhibit such a consideration. Earlier research has suggested that a sampling strategy (i.e., one that is based on a t-way parameter interaction) can be effective. As a result, many helpful t-way sampling strategies have been developed and can be found in the literature.Several advances have been achieved in the last 15 years, which have, in particular, served to facilitate the test planning process by systematically minimizing the test size required (based on certain t-way parameter interactions). Despite this significant progress, the integration and automation of strategies (from planning process to execution) are still lacking. Additionally, strategizing to sample (and construct) a minimum test set from the exhaustive test space is an NP-complete problem; that is, it is often unlikely that an efficient strategy exists that could regularly generate an optimal test set. Motivated by these challenges, this paper discusses the design, implementation, and validation of an efficient strategy for t-way testing, the GTWay strategy. The main contribution of GTWay is the integration of t-way test data generation with automated (concurrent) execution as part of its tool implementation. Unlike most previous methods, GTWay addresses the generation of test data for a high coverage strength (t > 6).

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Assessing IRPS as an efficient pairwise test data generation strategy

Cited by 11 publications

References 19 publications

Improved pairwise test suites for non‐prime‐power orders

Improved pairwise test suites for non‐prime‐power orders

Construction of Strength Two Mixed Covering Arrays Using Greedy Mutation in Genetic Algorithm

Design and implementation of a t-way test data generation strategy with automated execution tool support

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