Who's this? developer identification using IDE event data

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...

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Bibliographic Details
Published in:	2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR) pp. 90 - 93
Main Authors:	Wilkie, John, Halabi, Ziad Al, Karaoglu, Alperen, Liao, Jiafeng, Ndungu, George, Ragkhitwetsagul, Chaiyong, Paixao, Matheus, Krinke, Jens
Format:	Conference Proceeding
Language:	English
Published:	New York, NY, USA ACM 28-05-2018
Series:	ACM Conferences
Subjects:	Classification algorithms Data mining Integrated Development Environment Kernel Machine Learning Recommendation Systems Software and its engineering > Software notations and tools Software and its engineering > Software notations and tools > Development frameworks and environments Support vector machines Testing Training machine learning recommendation systems integrated development environment
Online Access:	Get full text
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Summary:	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.
ISBN:	9781450357166 1450357164
ISSN:	2574-3864
DOI:	10.1145/3196398.3196461