Existing mutation techniques produce vast numbers of equivalent, trivial, and redundant mutants. Selective mutation strategies aim to reduce the inherent redundancy of full mutation analysis to obtain most of its benefit for a fraction of the cost. Unfortunately, recent research has shown that there is no fixed selective mutation strategy that is effective across a broad range of programs; the utility (i.e., usefulness) of a mutant produced by a given mutation operator varies greatly across programs.

This paper hypothesizes that mutant utility, in terms of equivalence, triviality, and dominance, can be predicted by incorporating context information from the program in which the mutant is embedded. Specifically, this paper (1) explains the intuition behind this hypothesis with a motivational example, (2) proposes an approach for modeling program context using a program’s abstract syntax tree, and (3) proposes and evaluates a series of program-context models for predicting mutant utility. The results for 129 mutation operators show that program context information greatly increases the ability to predict mutant utility. The results further show that it is important to consider program context for individual mutation operators rather than mutation operator groups.