Automating Program Structure Classification

• URL: http://arxiv.org/abs/2101.10087v1
• Date: Fri, 15 Jan 2021 21:24:37 GMT
• Title: Automating Program Structure Classification
• Authors: Will Crichton, Georgia Gabriela Sampaio, Pat Hanrahan
• Abstract summary: We show how supervised machine learning methods can automatically classify student programs into a predetermined set of high-level structures. We demonstrate that these models can achieve 91% classification accuracy when trained on 108 programs.
• Score: 6.215059642140581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When students write programs, their program structure provides insight into their learning process. However, analyzing program structure by hand is time-consuming, and teachers need better tools for computer-assisted exploration of student solutions. As a first step towards an education-oriented program analysis toolkit, we show how supervised machine learning methods can automatically classify student programs into a predetermined set of high-level structures. We evaluate two models on classifying student solutions to the Rainfall problem: a nearest-neighbors classifier using syntax tree edit distance and a recurrent neural network. We demonstrate that these models can achieve 91% classification accuracy when trained on 108 programs. We further explore the generality, trade-offs, and failure cases of each model.

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