G2Way A Backtracking Strategy for Pairwise Test Data Generation

Klaib, Mohammad F. J.; Zamli, Kamal Z.; Isa, Nor Ashidi Mat; Younis, Mohammed Issam; Abdullah, Rusli

doi:10.1109/apsec.2008.49

Cited by 40 publications

(21 citation statements)

References 14 publications

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“…Over the years, a number of variations of AETG have emerged including mAETG [27] and mAETG_SAT [34]. Similar to AETG, GTWay [35,36] also adopts the one-test-at-a-time approach to generate the final test suite. Unlike AETG, GTWay permits the use of actual parameter values as a symbolic string and supports automated execution of test cases.…”

Section: General Computational-based Strategiesmentioning

confidence: 99%

A Tabu Search hyper-heuristic strategy for t-way test suite generation

Zamli

Alkazemi

Kendall

2016

Applied Soft Computing

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101

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This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning Based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks.

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Section: General Computational-based Strategiesmentioning

confidence: 99%

A Tabu Search hyper-heuristic strategy for t-way test suite generation

Zamli

Alkazemi

Kendall

2016

Applied Soft Computing

Self Cite

101

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“…Wong et al (2010) investigate a new fault localization method using code coverage heuristic. In other recently researches, interaction testing using CAs have been effective for improving code coverage using some empirical studies (Zamli et al, 2011;Klaib et al, 2008). This in turn could be an important motivation for using CAs with code coverage heuristics to improve the fault localization as conducted by Wong et al (2010).…”

Section: Future Research Directionsmentioning

confidence: 99%

A Review of Covering Arrays and Their Application to Software Testing

Ahmed¹,

Zamli²

2011

Journal of Computer Science

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Problem statement: As a complex logic system, software may suffer from different source of faults. Those faults can be avoided by applying different testing processes. It appears recently that the interaction among the system factors represents a common source of faults. Software function properly, all input factors and their interactions of the software need to be tested i.e., exhaustive testing. Random testing, in another hand, doesn't guarantee the coverage of all factors interaction. Approach: Covering Arrays (CAs) are mathematical objects used as platform or structure to represent the interactions of factors for a given system. The uses of CAs become important to reduce the test cases by covering all t-interactions of the system factors at least one time. Results: This study focuses exclusively on the applications of the CAs in software interaction testing. We provide an overview of CAs notations, types and construction methods. Conclusion: We reviewed the recent applications of CAs to software testing and discuss the future possible directions of the research. The research in this area seems to be an active research direction for the coming years.

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“…Testing helps to reveal the hidden problems in the product, which otherwise goes unnoticed providing a false sense of well-being. It is said to cover 40 to 50 percent of the development cost and resources [6,7]. Although important to quality and widely deployed by programmers and testers, testing still remains an art.…”

Section: Introductionmentioning

confidence: 99%

“…The failure of any system may be catastrophic that we may lose very important data or fortunes or sometimes even lives [7,8]. The main reason for failure is the lack of proper testing.…”

Section: Introductionmentioning

confidence: 99%

“…If the time required for one test to be executed is 5 minutes, then it would take nearly 10 years for a complete test to be done. Thus, the amount of resources consumed for a complete and exhaustive testing of the system becomes unreasonable and unaffordable [12,13]. While it is vital to assure the quality and the reliability of the system, it is impossible to do an exhaustive testing due to the combinatorial explosion problem.…”

Section: Introductionmentioning

confidence: 99%

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Anatomy of the Tree Based Strategy for High Strength Interaction Testing

Klaib¹

2015

The 7th International Conference on Information Technology

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Abstract-The amount of resources consumed for a complete and exhaustive testing becomes unreasonable and unaffordable. While it is vital to assure the quality and the reliability of any system, it is impossible to do an exhaustive testing due to the huge number of possible combinations. To bring a balance between exhaustive testing and lack of testing combinatorial interactions testing has been adopted. Although it is stated in literature that a complete pairwise interaction testing ensures the detection of 50-97 percent of faults, it is not sufficient to stop with pairwise testing alone for highly interactive systems. Therefore, there is a need to extend the level of testing for a general multi way combinatorial interactions testing. This paper enhanced the previous strategies "A tree based strategy for test data generation and cost calculation" and "3-way interaction testing using the tree strategy" to support a general multi-way combinatorial interaction testing involving uniform and non uniform parametric values. In this strategy, two algorithms have been adopted; a tree construction algorithm which constructs the possible test cases and an iterative cost calculation algorithm that constructs efficient multiway test suites which cover all parameter interactions between input components. Both algorithms are presented in details.

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G2Way A Backtracking Strategy for Pairwise Test Data Generation

Cited by 40 publications

References 14 publications

A Tabu Search hyper-heuristic strategy for t-way test suite generation

A Tabu Search hyper-heuristic strategy for t-way test suite generation

A Review of Covering Arrays and Their Application to Software Testing

Anatomy of the Tree Based Strategy for High Strength Interaction Testing

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