Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
| Publication date | 23/11/2021 |
|---|---|
| Host publication | Product-Focused Software Process Improvement - 22nd International Conference, PROFES 2021, Proceedings: 22nd International Conference, PROFES 2021, Turin, Italy, November 26, 2021, Proceedings |
| Editors | Luca Ardito, Andreas Jedlitschka, Maurizio Morisio, Marco Torchiano |
| Place of Publication | Cham |
| Publisher | Springer |
| Pages | 199-206 |
| Number of pages | 8 |
| ISBN (electronic) | 9783030914523 |
| ISBN (print) | 9783030914516 |
| <mark>Original language</mark> | English |
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13126 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
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.