Incremental test case generation using bounded model checking: an application to automatic rating

Abstract

In this paper we focus on the task of rating solutions to a programming exercise. State-of-the-art rating methods generally examine each solution against an exhaustive set of test cases, typically designed manually. Hence an issue of completeness arises. We propose the application of bounded model checking to the automatic generation of test cases. The experimental evaluation we have performed reveals a substantial increase in accuracy of ratings at a cost of a moderate increase in computation resources needed. Most importantly, application of model checking leads to the finding of errors in solutions that would previously have been classified as correct.

Cheating Detection Pipeline for Online Interviews

“…In [8], [9], [10] authors propose online exam environments that can prevent cheating actions with preemptive procedures, so that without any proctor they can minimize the number of cheating attempts. In another variation of this approach, preemptive systems are combined with human proctors as in [11], [12] There are automated interview systems that do not have AI-based anti-cheating services like [13], [14]. These systems were designed for an online coding platform to evaluate the candidates.…”

“…These systems were designed for an online coding platform to evaluate the candidates. Although [13] and [14] have plagiarism services, there is no detection for the presence of multiple persons or electronic device usage. Some approaches like [15] combine autonomous detection systems with human proctors with well-defined pipelines.…”