Simulating Student Mistakes to Evaluate the Fairness of Automated Grading
Benjamin Clegg, Siobhán North, Phil McMinn, Gordon Fraser · 2019
The use of autograding to assess programming students may lead to unfairness if an autograder is incorrectly configured. Mutation analysis offers a potential solution to this problem. By simulating student coding mistakes, an automated technique can evaluate the fairness and completeness of an autograding configuration. 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.