Heuristic Approach of Automated Test Data Generation for Programs Having Array of Different Dimensions and Loops with Variable Number of Iteration

Tahbildar, Hitesh; Kalita, Bichitra

doi:10.5121/ijsea.2010.1405

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…They are TriangleClassification [27], [3], [28], [9], [13], MaxMin [27], BubbleSort [27], [9], [29], MergeSort [29], GCD [9], and Month. They are described in detail below.…”

Section: A Benchmark Test Programsmentioning

confidence: 99%

Code coverage optimisation in genetic algorithms and particle swarm optimisation for automatic software test data generation

Koleejan

Xue

Zhang

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Automatic software test data generation is the process of generating a set of test cases for a given program which can achieve a high code coverage. Genetic algorithms (GAs) and particle swarm optimisation (PSO) can automatically evolve a set of test data, but the traditional representation in GAs and PSO produces solutions with a single set of data cases, which may not achieve good performance on programs with many complex conditions. This paper proposes a multi-vector representation in GAs and PSO, which can generate multiple sets of data cases in a single run, to generate test data for complex test programs. Experiments have been conducted to examine and compare the performance of GAs and PSO on six commonly used benchmark test programs and three newly developed programs with a relatively large number of complex conditions. The experimental results show that the proposed multi-vector representation can improve the performance of GAs and PSO on all the nine tested programs, achieving the optimal 100% code coverage on the relatively easy programs. PSO outperforms GAs in terms of both the code coverage and the computational efficiency, especially on the hard programs.

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“…They are TriangleClassification [27], [3], [28], [9], [13], MaxMin [27], BubbleSort [27], [9], [29], MergeSort [29], GCD [9], and Month. They are described in detail below.…”

Section: A Benchmark Test Programsmentioning

confidence: 99%

Code coverage optimisation in genetic algorithms and particle swarm optimisation for automatic software test data generation

Koleejan

Xue

Zhang

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

show abstract

“…The comparator compares the number of paths in current iteration and previous iteration to terminate with minimum number of iterations either for longest path criteria or all path criteria. Details of this model can be seen in [47]. This architecture is best suited for programs having loops with variable number of iterations and array with different dimensions.…”

Section: Architecture III Based On Both Symbolic and Actual Value Exementioning

confidence: 99%

“…But problem of this type of programming construct is that the number of path may increases exponentially with the increase of iteration and array size. [44,46] given heuristics to predict the minimum number of iteration required for better coverage. But it is difficult to set a common heuristic for all types of programming construct.…”

Section: Loops and Array Program Testingmentioning

confidence: 99%

“…This type of work is very useful for beginners to know how to start research. In 2010 Tahbildar etal [44] has given a heuristic to determine the number of iteration required for longest path coverage. In 2000-2010 Object oriented test data generation techniques are also taken as key area of research.…”

Section: Introductionmentioning

confidence: 99%

“…In 2008 Gautam Saha [3] made a map which gives us clear understanding of software testing types, concepts and their relationship which may help researcher to get a complete picture of the domain. In 2010 [47] proposed a heuristic specially useful programming construct having loops of different dimensions and array of variable length. Moreover, In 2010 [46] test data is generated by avoiding unsolvable constraints.…”

Section: Introductionmentioning

confidence: 99%

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