Mutation testing measures a test suite's ability to detect bugs by inserting bugs into the code and seeing if the tests behave differently. Mutation testing has recently seen increased adoption in industrial and open-source software but sees limited use in education. Some instructors use manually-constructed mutants to evaluate student tests and provide general automated feedback. Additional tutoring requires more intensive instructor interaction such as in office hours, which requires substantial resources at scale. Prior work suggests that students benefit from frequent, actionable feedback, and our work focuses on the challenge of leveraging automation to give students high-quality feedback when they need it.

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.

Botnets have become the top threat to Internet security. Botnets generate and transmit huge amounts of malicious traffic for various purposes. In this paper, we propose a dynamic programming based algorithm to calculate a set of appropriate routers for placing early filters of malicious traffic in the network in order to maximize the benefits of early malicious traffic filtering. Moreover, we discuss how to combine our early filtering approach and the QoS routing of traffic so that the bandwidth saved by early filtering of malicious traffic can be efficiently utilized by legitimate traffic.