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	Predictive mutation testing
	Zhang, Jie ; Wang, Ziyi ; Zhang, Lingming ; Hao, Dan ; Zang, Lei ; Cheng, Shiyang ; Zhang, Lu
	2016
英文摘要	Mutation testing is a powerful methodology for evaluating test suite quality. In mutation testing, a large number of mutants are generated and executed against the test suite to check the ratio of killed mutants. Therefore, mutation testing is widely believed to be a computationally expensive technique. To alleviate the efficiency concern of mutation testing, in this paper, we propose predictive mutation testing (PMT), the first approach to predicting mutation testing results without mutant execution. In particular, the proposed approach constructs a classification model based on a series of features related to mutants and tests, and uses the classification model to predict whether a mutant is killed or survived without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (i.e., cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while incurring only a small accuracy loss when predicting mutant execution results, indicating a good tradeoff between efficiency and effectiveness of mutation testing. ? 2016 ACM.; EI; 342-353
语种	英语
出处	25th International Symposium on Software Testing and Analysis, ISSTA 2016
DOI标识	10.1145/2931037.2931038
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449288]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Jie,Wang, Ziyi,Zhang, Lingming,et al. Predictive mutation testing. 2016-01-01.

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