Through studying on one of the reasons leading to the high cost of mutation testing which is the large number of mutants produced during the process of testing, a mutants reduction method of clustering based on genetic algorithm was proposed.Mutants with similar characteristics would be placed in the same cluster, and then randomly selected one from each cluster as a representative in order to reduce the mutants.The experimental results show that: 1) the proposed method can reduce mutants without compromising the adequacy of the constituted test suite; 2) and compared with K-means algorithm and agglomerative clustering algorithm, it can automatically form an appropriate number of clusters, and is more effective.%对导致变异测试高代价的原因之一——测试过程中容易产生数目庞大的变异体进行了研究,提出基于遗传算法聚类的变异体约简方法.把具有相似特征的变异体置于同一簇中,再从每个簇中随机选择一个作为代表,从而实现变异体的约简.实验表明:1)该方法可在不降低构造出的测试用例集的测试充分度的前提下,约简变异体;2)与K-means算法和凝聚型层次聚类算法相比,该方法能够在自动产生合适的聚类数目的同时,具有更优的约简效果.
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