The use of autograding to assess programming students may lead to unfair grading if an autograder is incorrectly configured. Mutation analysis offers a potential solution to this problem. By simulating student coding mistakes, the fairness and completeness of an autograding configuration can be evaluated. In this paper, we introduce a set of mutation operators to be used in such a technique, derived from a mistake classification of real student solutions for two introductory programming tasks.