Exploring the Impact of Code Style in Identifying Good Programmers


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.

International Workshop on Quantitative Approaches to Software Quality
Rafed Muhammad Yasir
Rafed Muhammad Yasir
Grad Student

My interests include Software Engineering, DevOps Lifecycle, Cybersecurity and Machine Learning.