Abstract
Understanding and recognising the difference between novice and expert programmers could be beneficial in a wide range of scenarios, such as to screen programming job applicants. In this paper, we explore the identification of code author attributes to enable novice/expert differentiation via machine learning models. Our iteratively developed model is based on data from HackerRank, a competitive programming website. Multiple experiments were carried using 10-fold cross-validation. Our final model performed well by differentiating novice coders from expert coders with 71.3% accuracy.
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Lee, C.H., Hall, T. (2021). Using Machine Learning to Recognise Novice and Expert Programmers. In: Ardito, L., Jedlitschka, A., Morisio, M., Torchiano, M. (eds) Product-Focused Software Process Improvement. PROFES 2021. Lecture Notes in Computer Science(), vol 13126. Springer, Cham. https://doi.org/10.1007/978-3-030-91452-3_13
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DOI: https://doi.org/10.1007/978-3-030-91452-3_13
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