Automatic programming error class identification with code plagiarism-based clustering
Sébastien Combéfis, Arnaud Schils · 2016
Online platforms to learn programming are very popular nowadays. These platforms must automatically assess codes submitted by the learners and must provide good quality feedbacks in order to support their learning. Classical techniques to produce useful feedbacks include using unit testing frameworks to perform systematic functional tests of the submitted codes or using code quality assessment tools. This paper explores how to automatically identify error classes by clustering a set of submitted codes, using code plagiarism detection tools to measure the similarity between the codes. The proposed approach and analysis framework are presented in the paper, along with a first experiment using the Code Hunt dataset.