Work-in-Progress: Python Code Critiquer, a Machine Learning Approach.

This research is part of a larger development project that is working on a multi-programming language code critiquer called WebTA. The WebTA code-critiquing software is designed to be used in courses for novice programmers, e.g., CS1 a first engineering course. The authors report on a component of the project that makes initial steps towards a automating the identification of common student mistakes, or antipatterns in code. Antipatterns can be errors, inefficiencies, or incorrect style choices in the code. This works is aimed at Python and uses the machine learning algorithm, Random Forests, to identify a stylistic antipattern of crowded operators.