Wilkie, J;
Al Halabi, Z;
Karaoglu, A;
Liao, J;
Ndungu, G;
Ragkhitwetsagul, C;
Paixao, M;
(2018)
Who's this? Developer identification using IDE event data.
In: Zaidman, A and Kamei, Y and Hill, E, (eds.)
MSR '18: Proceedings of the 15th International Conference on Mining Software Repositories.
(pp. pp. 90-93).
Association for Computing Machinery (ACM): New York, NY, USA.
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Abstract
This paper presents a technique to identify a developer based on their IDE event data. We exploited the KaVE data set which recorded IDE activities from 85 developers with 11M events. We found that using an SVM with a linear kernel on raw event count outperformed k-NN in identifying developers with an accuracy of 0.52. Moreover, after setting the optimal number of events and sessions to train the classifier, we achieved a higher accuracy of 0.69 and 0.71 respectively. The findings shows that we can identify developers based on their IDE event data. The technique can be expanded further to group similar developers for IDE feature recommendations.
| Type: | Proceedings paper |
|---|---|
| Title: | Who's this? Developer identification using IDE event data |
| Event: | 15th International Conference on Mining Software Repositories (MSR '18) |
| Location: | Gothenburg, Sweden |
| Dates: | 28 May 2018 - 29 May 2018 |
| ISBN-13: | 978-1-4503-5716-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3196398.3196461 |
| Publisher version: | https://doi.org/10.1145/3196398.3196461 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Machine Learning, Integrated Development Environment, Recommendation Systems |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10046216 |
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