Computer Science ›› 2017, Vol. 44 ›› Issue (3): 209-214.doi: 10.11896/j.issn.1002-137X.2017.03.044

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Research on Test Data Automatic Generation Based on Improved Genetic Algorithm

GAO Xue-di, ZHOU Li-juan, ZHANG Shu-dong and LIU Hao-ming   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Automatic test data generation is the basis of software testing,and it is also a key link in the process of test automation technology.In order to improve the efficiency of testing automation,a new algorithm was proposed to improve the traditional genetic algorithm based on the combination of test data automatic generation system model.The adaptive crossover operator and mutation operator are used in this algorithm,and the improved simulated annealing mechanism is introduced to improve it.At the same time,the algorithm is also designed to fit the fitness function to accelerate the optimization process of the data.Through the triangle program,binary search and bubble sort program,the basic genetic algorithm and the adaptive genetic algorithm were compared,and the performance test was done for improved algorithm.Experimental results show the practicability as well as feasibility and efficiency of the algorithm in the test data generation.

Key words: Software test,Generic algorithm,Hamming function,Automatic test data generation

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