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- W2883179942 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." @default.
- W2883179942 created "2018-08-03" @default.
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- W2883179942 date "2018-05-28" @default.
- W2883179942 modified "2023-09-24" @default.
- W2883179942 title "Who's this?" @default.
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- W2883179942 doi "https://doi.org/10.1145/3196398.3196461" @default.
- W2883179942 hasPublicationYear "2018" @default.
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