Abstract: 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.

Key words: mutation testing, genetic algorithm, clustering technique, mutants reduction

摘要： 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。

关键词: 变异测试, 遗传算法, 聚类技术, 变异体约简