A common practice in computer science courses is to evaluate student-written test suites against either a set of manually-seeded faults (handwritten by an instructor) or against all other student-written implementations (``all-pairs'' grading). However, manually seeding faults is a time consuming and potentially error-prone process, and the all-pairs approach requires significant manual and computational effort to apply fairly and accurately. Mutation analysis, which automatically seeds potential faults in an implementation, is a possible alternative to these test suite evaluation approaches. Although there is evidence in the literature that mutants are a valid substitute for real faults in large open-source software projects, it is unclear whether mutants are representative of the kinds of faults that students make. If mutants are a valid substitute for faults found in student-written code, and if mutant detection is correlated with manually-seeded fault detection and faulty student implementation detection, then instructors can instead evaluate student test suites using mutants generated by open-source mutation analysis tools.

Using a dataset of 2,711 student assignment submissions, we empirically evaluate whether mutation score is a good proxy for manually-seeded fault detection rate and faulty student implementation detection rate. Our results show a strong correlation between mutation score and manually-seeded fault detection rate and a moderately strong correlation between mutation score and faulty student implementation detection. We identify a handful of faults in student implementations that, to be coupled to a mutant, would require new or stronger mutation operators or applying mutation operators to an implementation with a different structure than the instructor-written implementation. We also find that this correlation is limited by the fact that faults are not distributed evenly throughout student code, a known drawback of all-pairs grading. Our results suggest that mutants produced by open-source mutation analysis tools are of equal or higher quality than manually-seeded faults and a reasonably good stand-in for real faults in student implementations. Our findings have implications for software testing researchers, educators, and tool builders alike.