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.

DOI

Bibtex Entry

@inproceedings{Clegg2019,
  author    = "Clegg, Benjamin and Fraser, Gordon and North, Siobh\'{a}n and McMinn, Phil",
  title     = "Simulating Student Mistakes to Evaluate the Fairness of Automated Grading",
  booktitle = "International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET 2019)",
  pages     = "121--125",
  year      = "2019"
}