Adaptive Random Test Case Generation for Combinatorial Testing

Huang, Rubing; Xie, Xue‐Jun; Chen, Tsong Yueh; Lu, Yilong

doi:10.1109/compsac.2012.15

Cited by 27 publications

(29 citation statements)

References 21 publications

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“…In CIT, a distance measure is widely used to construct and prioritize CIT test suite (CA, MCA, or VCA), that is uncovered t-wise value combinations distance or UVCD [16].…”

Section: A Cit and Cit Test Suitementioning

confidence: 99%

Prioritizing Variable-Strength Covering Array

Huang

Chen

Zhang

et al. 2013

2013 IEEE 37th Annual Computer Software and Applications Conference

Self Cite

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Combinatorial interaction testing is a well-studied testing strategy, and has been widely applied in practice. Combinatorial interaction test suite, such as fixed-strength and variable-strength interaction test suite, is widely used for combinatorial interaction testing. Due to constrained testing resources in some applications, for example in combinatorial interaction regression testing, prioritization of combinatorial interaction test suite has been proposed to improve the efficiency of testing. However, nearly all prioritization techniques may only support fixed-strength interaction test suite rather than variable-strength interaction test suite. In this paper, we propose two heuristic methods in order to prioritize variablestrength interaction test suite by taking advantage of its special characteristics. The experimental results show that our methods are more effective for variable-strength interaction test suite by comparing with the technique of prioritizing combinatorial interaction test suites according to test case generation order, the random test prioritization technique, and the fixed-strength interaction test suite prioritization technique. Besides, our methods have additional advantages compared with the prioritization techniques for fixed-strength interaction test suite.

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“…In CIT, a distance measure is widely used to construct and prioritize CIT test suite (CA, MCA, or VCA), that is uncovered t-wise value combinations distance or UVCD [16].…”

Section: A Cit and Cit Test Suitementioning

confidence: 99%

Prioritizing Variable-Strength Covering Array

Huang

Chen

Zhang

et al. 2013

2013 IEEE 37th Annual Computer Software and Applications Conference

Self Cite

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“…For instance, AETG [14] (resp. Huang's method [25]) constructs each test case for pairwise testing by first generating r different candidate test cases using a greedy algorithm (resp. randomly) and choosing one that covers the most new parameter-value pairs.…”

Section: A Proposed Approach: Dicotmentioning

confidence: 99%

“…Huang et al [25] integrated the notion of t-way coverage into ART, and used the number of newly covered parametervalue t-tuples as a distance metric. Their method randomly generates test case candidates, and chooses a test case with the maximum distance, i. e., the maximum number of newly covered parameter-value t-tuples.…”

Section: Related Workmentioning

confidence: 99%

“…The approaches by Bryce et al [8] and Huang et al [25] both interpret the number of new parameter-value t-tuples as the test case distance to generate t-way test suites, and do not care about t (> t)-way coverage. On the other hand, we integrate increasing the distance and increasing the new parameter-value t-tuples so as to improve t (> t)-way coverage.…”

Section: Related Workmentioning

confidence: 99%

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Distance-Integrated Combinatorial Testing

Choi

Artho

Kitamura

et al. 2016

2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE)

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Abstract-This paper proposes a novel approach to combinatorial test generation, which achieves an increase of not only the number of new combinations but also the distance between test cases. We applied our distance-integrated approach to a stateof-the-art greedy algorithm for traditional combinatorial test generation by using two distance metrics, Hamming distance, and a modified chi-square distance. Experimental results using numerous benchmark models show that combinatorial test suites generated by our approach using both distance metrics can improve interaction coverage for higher interaction strengths with low computational overhead.

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“…According to Huang et al (2012), ART-based tests cover all t-way interactions more quickly than randomly chosen tests. At the same time they often detect more failures earlier and with fewer test cases.…”

Section: Fault Detectionmentioning

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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Adaptive Random Test Case Generation for Combinatorial Testing

Cited by 27 publications

References 21 publications

Prioritizing Variable-Strength Covering Array

Prioritizing Variable-Strength Covering Array

Distance-Integrated Combinatorial Testing

Test Algebra for Concurrent Combinatorial Testing

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