Is operator-based mutant selection superior to random mutant selection?

  • Authors:

  • Lu Zhang;Shan-Shan Hou;Jun-Jue Hu;Tao Xie;Hong Mei

  • Affiliations:

  • Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China;North Carolina State University, Raleigh, NC;Peking University, Beijing, China

  • Venue:
  • Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
  • Year:
  • 2010

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Abstract

Due to the expensiveness of compiling and executing a large number of mutants, it is usually necessary to select a subset of mutants to substitute the whole set of generated mutants in mutation testing and analysis. Most existing research on mutant selection focused on operator-based mutant selection, i.e., determining a set of sufficient mutation operators and selecting mutants generated with only this set of mutation operators. Recently, researchers began to leverage statistical analysis to determine sufficient mutation operators using execution information of mutants. However, whether mutants selected with these sophisticated techniques are superior to randomly selected mutants remains an open question. In this paper, we empirically investigate this open question by comparing three representative operator-based mutant-selection techniques with two random techniques. Our empirical results show that operator-based mutant selection is not superior to random mutant selection. These results also indicate that random mutant selection can be a better choice and mutant selection on the basis of individual mutants is worthy of further investigation.