This case study reports on two first-semester programming courses with more than 190 students. Both courses made use of automated assessments. We observed how students trick these systems by analysing the version history of suspect submissions. By analysing more than 3300 submissions, we revealed four astonishingly simple tricks (overfitting, evasion) and cheat-patterns (redirection, and injection) that students used to trick automated programming assignment assessment systems (APAAS). Although not the main focus of this study, it discusses and proposes corresponding counter-measures where appropriate.
Nevertheless, the primary intent is to raise problem awareness and to identify and systematise observable problem patterns in a more formal approach. The identified immaturity of existing APAAS solutions might have implications for courses that rely deeply on automation like MOOCs. Therefore, we conclude to look at APAAS solutions much more from a security point of view (code injection). Moreover, we identify the need to evolve existing unit testing frameworks into more evaluation-oriented teaching solutions that provide better trick and cheat detection capabilities and differentiated grading support.
A preprint of the paper can be found here: https://www.researchgate.net/publication/330752881
Submitted April 04, 2019 at 10:35PM by nkode http://bit.ly/2IdN9Id
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