Toward automatic feedback of coding style for programming courses

Abstract

In this research, we developed a methodology for automatic evaluation of coding style and estimate its effectiveness compared to human evaluation. To achieve this, we developed 179 features spanning 8 categories to capture code structure and other stylistic properties. Results based on a set of student assignments and code from a Python textbook validate the features as effective in classification tasks. The features were further used to optimise classifiers to predict the teacher's evaluation of the student code and obtain good classification accuracy. The proposed feature set and experimental results provide a first step towards providing students with automatic coding style feedback.