Fuzz Testing Projects in Massive Courses

Abstract

Scaffolded projects with automated feedback are core instructional components of many massive courses. In subjects that include programming, feedback is typically provided by test cases constructed manually by the instructor. This paper explores the effectiveness of fuzz testing, a randomized technique for verifying the behavior of programs. In particular, we apply fuzz testing to identify when a student's solution differs in behavior from a reference implementation by randomly exploring the space of legal inputs to a program. Fuzz testing serves as a useful complement to manually constructed tests. Instructors can concentrate on designing targeted tests that focus attention on specific issues while using fuzz testing for comprehensive error checking. In the first project of a 1,400-student introductory computer science course, fuzz testing caught errors that were missed by a suite of targeted test cases for more than 48% of students. As a result, the students dedicated substantially more effort to mastering the nuances of the assignment.