Using arti .cial lifetechniques to generatetest cases forcombinatorial testing

Shiba, Tadayoshi; Tsuchiya, Tatsuhiro; Kikuno, Tohru

doi:10.1109/cmpsac.2004.1342808

Cited by 121 publications

(143 citation statements)

References 10 publications

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“…Bansal et al [21] proposed an approach to generate pairwise test cases using the GA. The authors generated the initial solution using the Hamming distance.…”

Section: Related Workmentioning

confidence: 99%

“…For chromosome representation, integer array encoding is used as suggested in [18]. In a GA an initial solution can be generated randomly [18] or by using techniques such as Hamming distance [21], Euclidean distance [6], etc.…”

Section: Creating An Initial Populationmentioning

confidence: 99%

“…The IPO algorithm generates all t-sets for the first t factors and then incrementally expands the solution, both horizontally and vertically, until the array is complete [11]. Metaheuristic algorithms include tabu search [6], simulated annealing [12], ant colony optimization (ACO) [13], particle swarm optimization (PSO) [14,15], GA [16][17][18][19][20][21][22], Harmony Search [23,24], etc. The hybrid approach combines different methods to efficiently generate a small test suite.…”

Section: Related Workmentioning

confidence: 99%

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Test Set Generation for Pairwise Testing Using Genetic Algorithms

2015

J Inf Process Syst

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In software systems, it has been observed that a fault is often caused by an interaction between a small number of input parameters. Even for moderately sized software systems, exhaustive testing is practically impossible to achieve. This is either due to time or cost constraints. Combinatorial (t-way) testing provides a technique to select a subset of exhaustive test cases covering all of the t-way interactions, without much of a loss to the fault detection capability. In this paper, an approach is proposed to generate 2-way (pairwise) test sets using genetic algorithms. The performance of the algorithm is improved by creating an initial solution using the overlap coefficient (a similarity matrix). Two mutation strategies have also been modified to improve their efficiency. Furthermore, the mutation operator is improved by using a combination of three mutation strategies. A comparative survey of the techniques to generate t-way test sets using genetic algorithms was also conducted. It has been shown experimentally that the proposed approach generates faster results by achieving higher percentage coverage in a fewer number of generations. Additionally, the size of the mixed covering arrays was reduced in one of the six benchmark problems examined.

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“…Bansal et al [21] proposed an approach to generate pairwise test cases using the GA. The authors generated the initial solution using the Hamming distance.…”

Section: Related Workmentioning

confidence: 99%

Section: Creating An Initial Populationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Test Set Generation for Pairwise Testing Using Genetic Algorithms

2015

J Inf Process Syst

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“…The uncovered new t-way combinations are covered by AETG-GA. In each generation, the best chromosomes are kept and survive to the next generation Shiba et al (2004).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Test Algebra for Concurrent Combinatorial Testing

Tsai

2017

Combinatorial Testing in Cloud Computing

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A new algebraic system, Test Algebra (TA), is proposed for identifying faults in combinatorial testing for SaaS (Software-as-a-Service) applications. In the context of cloud computing, SaaS is a new software delivery model, in which mission-critical applications are composed, deployed, and executed on cloud platforms. Testing SaaS applications is challenging because new applications need to be tested once they are composed, and prior to their deployment. A composition of components providing services yields a configuration providing a SaaS application. While individual components in the configuration may have been thoroughly tested, faults still arise due to interactions among the components composed, making the configuration faulty.When there are k components, combinatorial testing algorithms can be used to identify faulty interactions for t or fewer components, for some threshold 2 ≤ t ≤ k on the size of interactions considered. In general these methods do not identify specific faults, but rather indicate the presence or absence of some fault. To identify specific faults, an adaptive testing regime repeatedly constructs and tests configurations in order to determine, for each interaction of interest, whether it is faulty or not. In order to perform such testing in a loosely coupled distributed environment such as the cloud, it is imperative that testing results can be combined from many different servers. The TA defines rules to permit results to be combined, and to identify the faulty interactions. Using the TA, configurations can be tested concurrently on different servers and in any order. The results, using the TA, remain the same.

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“…Recently, researchers have also started to adopt meta-heuristics based strategies including that of Genetic Algorithm (GA) GA (Sthamer 1995;Shiba, Tsuchiya et al 2004;Bryce and Colbourn 2007;Afzal, Torkar et al 2009;Chen, Gu et al 2009;McCaffrey 2010), Ant Colony (ACS) ACA (Harman and Jones 2001;Shiba, Tsuchiya et al 2004;Wang, Xu et al 2008;Afzal, Torkar et al 2009;Chen, Gu et al 2009), Particle Swarm Optimization (PSTG) PSTG Ahmed and Zamli 2011;Ahmed, Zamli et al 2012), and Harmony Search Strategy (HSS) (Alsewari and Zamli 2012). Although these aforementioned strategies are useful, many of them fall short in term of the support for constraints (i.e.…”

Section: Introductionmentioning

confidence: 99%

Comparative Benchmarking of Constraints T-Way Test Generation Strategy Based on Late Acceptance Hill Climbing Algorithm

Zamli¹,

Alsewari²,

Alkazemi³

2015

ijsecs

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This paper describes the new t-way strategy based the Late Acceptance based Hill Climbing algorithm, called LAHC, for constraints t-way test generation. Unlike earlier competing work, LAHC does not require significant tuning in order to have it working. In fact, LAHC merely requires minor adjustment of the common controlling parameters involving iteration and population size depending on the given system configuration. Our benchmarking results have been promising as LAHC gives competitive results in most constraints configurations considered.

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Using arti .cial lifetechniques to generatetest cases forcombinatorial testing

Cited by 121 publications

References 10 publications

Test Set Generation for Pairwise Testing Using Genetic Algorithms

Test Set Generation for Pairwise Testing Using Genetic Algorithms

Test Algebra for Concurrent Combinatorial Testing

Comparative Benchmarking of Constraints T-Way Test Generation Strategy Based on Late Acceptance Hill Climbing Algorithm

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