Computer Science ›› 2018, Vol. 45 ›› Issue (1): 249-254.doi: 10.11896/j.issn.1002-137X.2018.01.044

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Test Case Selection Technique Based on Semi-supervised Clustering Method

CHENG Xue-mei, YANG Qiu-hui, ZHAI Yu-peng and CHEN Wei   

  • Online:2018-01-15 Published:2018-11-13

Abstract: The purpose of regression testing is to ensure that no new faults are introduced into software after modifications.The case of regression test is increasing with the evolution of software,the software of so test selection techniques are used to control costs.In recent years,cluster analysis techniques are applied to test selection problem.We proposed a novel method called discriminative semi-supervised K-means clustering method (DSKM),which introduces semi-supervised learning clustering technology.Through DSKM,hidden pairwise constraints information is mined from the test execution history.By taking advantage of a large number of unlabeled samples and a small amount of labeled samples,DSKM can optimize the results of the cluster,and further optimize test case selection results.Experiment shows that compared with Constrained-Kmeans algorithm and SSKM method,DSKM is more effective.

Key words: Regression testing,Test case selection,K-means algorithm,Pairwise constraints,Linear discriminant analysis,Semi-supervised clustering

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