Exploring the Impact of Code Style in Identifying Good Programmers

TL;DR

This paper investigates whether code style can serve as an indicator of programmer quality by analyzing data from Google Code Jam with machine learning models, finding that stylistic features can predict good programmers.

Contribution

It is the first study to explore the use of code style as a predictor of programmer quality using machine learning techniques.

Findings

Good programmers can be identified using stylistic features.

No specific style groups are associated with good programmers.

Supervised models achieve meaningful prediction performance.

Abstract

Code style is an aesthetic choice exhibited in source code that reflects programmers individual coding habits. This study is the first to investigate whether code style can be used as an indicator to identify good programmers. Data from Google Code Jam was chosen for conducting the study. A cluster analysis was performed to find whether a particular coding style could be associated with good programmers. Furthermore, supervised machine learning models were trained using stylistic features and evaluated using recall, macro-F1, AUC-ROC and balanced accuracy to predict good programmers. The results demonstrate that good programmers may be identified using supervised machine learning models, despite that no particular style groups could be attributed as a good style.