Using Machine Learning to Recognise Novice and Expert Programmers

Lee, Chi Hong and Hall, Tracy (2021) Using Machine Learning to Recognise Novice and Expert Programmers. In: Product-Focused Software Process Improvement - 22nd International Conference, PROFES 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer, Cham, pp. 199-206. ISBN 9783030914516

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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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700
Subjects:
ID Code:
166448
Deposited By:
Deposited On:
22 Jun 2022 08:00
Refereed?:
Yes
Published?:
Published
Last Modified:
21 Nov 2022 17:42