Improving GA based automated test data generation technique for object oriented software

Gupta, Nirmal Kumar; Rohil, Mukesh Kumar

doi:10.1109/iadcc.2013.6514229

Cited by 10 publications

(4 citation statements)

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“…Nirmal Kumar Gupta have proposed a strategy that uses genetic algorithm to reduce the number of unfeasible test cases and establishing genetic algorithm to generate suitable test cases and hence improving the genetic algorithm .The genetic algorithm is used to generate the test cases for Object Oriented Software that is for java programs. But practically to use genetic algorithm there is a problem as there is local convergence problem [4].…”

Section: Review Of Literaturementioning

confidence: 99%

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Study of Test Case Selection and Prioritization

Kire¹,

Malhotra²

2014

IJCA

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Software testing is very important for ensuring software quality in organizations. In fact, the quality of test suite plays an important role for the success of software testing .Ant colony optimization (ACO) which is a part of Swarm Intelligence is used to settle optimization problem. This paper attempts to provide ideas of Test Case Selection and Prioritization which Ant Colony Optimization which can be helpful for the researchers who are working on software testing. There is a great expectation in the field of Swarm Intelligence as it always comes with a challenging thought. ACO is one of such promising approaches for its application in Software Testing.

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Section: Review Of Literaturementioning

confidence: 99%

“…It is not suitable for global search. [4] 4.The limitation of Bee Colony Optimization is that it is complicated to map the waggle dance to the solution, it requires deep knowledge of mathematics and one must have the knowledge of factors such as waggle dance, food source quality etc.…”

Section: Comparison Of Aco With Gasa and Psomentioning

confidence: 99%

Study of Test Case Selection and Prioritization

Kire¹,

Malhotra²

2014

IJCA

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“…There are two basic methods which can be used to identify test cases, functional testing (also known as black-box testing) [1][2][3] and structural testing (also known as white-box testing) [4][5][6]. Therefore, the technology of test data generation is usually divided into function-oriented [7,8] and structure-oriented [9,10] test data generation technology.…”

Section: Introductionmentioning

confidence: 99%

Improvement of the Baseline Method in Structural Testing

Chen¹,

Hao²,

Xie³

2015

TOAUTOCJ

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An algorithm based on the generation for baseline path is proposed in this paper. Making use of it, the original baseline path can be generated randomly and automatically. In respect of traversing the basic path group, the algorithm introduces the method of depth-first search to avoid storing more information of branch node and decrease the time of linear traversal. Using the column with S-node with no in-degree and the row with E-node with no out-degree, the case of nodes' value and the number of value cases are stored for reducing the waste in storage. Furthermore, giving priority to the behind path-node when generating a path, the time of judgment for loops is effectively reduced. Theoretical analysis and experimental results show that this new algorithm has an advantage over the original Baseline Method in efficiency and space complexity of the test path generation. The average space complexity is decreased from O(n) to O(log n) , and the time complexity is also reduced to a certain extent.

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“…Os resultados da análise exploratória indicaram um gap na pesquisa em geração de dados de teste para programas de PI, além de uma tendência dos estudos em utilizar AGs para gerar dados de teste ou apresentar revisões sistemáticas sobre o tema(ALLI et al, 2010;AFZAL;TORKAR;FELDT, 2009;DAVE;AGRAWAL, 2015;GUPTA;ROHIL, 2013). Por esses motivos, decidiu-se por um aprofundamento na literatura, por meio da condução de um mapeamento sistemático, conforme apresentado a seguir.…”

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TAIGA: uma abordagem para geração de dados de teste por meio de algoritmo genético para programas de processamento de imagens

Rodrigues¹

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Agradeço a Deus. Agradeço aos meus pais eà minha irmã por me apoiarem constantemente, apesar de todas as incertezas. Agradeçoà minha orientadora, professora Fátima, pela companhia, pelos conselhos, pela compreensão e por sempre me incentivar a ir além. Agradeço ao professor Delamaro por sua grande contribuição no desenvolvimento deste trabalho. Resumo RODRIGUES, Davi Silva. TAIGA: uma abordagem para geração de dados de teste por meio de algoritmo genético para programas de processamento de imagens. 2018. 127 f.

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Improving GA based automated test data generation technique for object oriented software

Cited by 10 publications

References 4 publications

Study of Test Case Selection and Prioritization

Study of Test Case Selection and Prioritization

Improvement of the Baseline Method in Structural Testing

TAIGA: uma abordagem para geração de dados de teste por meio de algoritmo genético para programas de processamento de imagens

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