May 6, 2019 · Our findings suggest that identifying problems within empirical software engineering that lend themselves to low-shot learning could accelerate ...

Conclusion Our findings suggest that identifying problems within empirical software engineering that lend themselves to low-shot learning could accelerate the ...

May 6, 2019 · Findings: We apply low-shot learning to the task of classifying UML class and sequence diagrams from Github, and demonstrate that surprisingly ...

This work applies low-shot learning to the task of classifying UML class and sequence diagrams from Github, and demonstrates that surprisingly good ...

Our findings suggest that identifying problems within empirical software engineering that lend themselves to low-shot learning could accelerate the adoption of ...

We apply low-shot learning to the task of classifying UML class and sequence diagrams from Github, and demonstrate that surprisingly good performance can be ...

Exploring the Applicability of Low-Shot Learning in Mining Software Repositories by Jordan Ott, Abigail Atchison, Erik J. Linstead published in.

Co-authors ; Exploring the applicability of low-shot learning in mining software repositories. J Ott, A Atchison, EJ Linstead. Journal of Big Data 6, 1-10, 2019.

Exploring the efficacy of transfer learning in mining image-based software ... Exploring the applicability of low-shot learning in mining software repositories.

Exploring the applicability of low-shot learning in mining software repositories. Authors. Jordan Ott; Abigail Atchison; Erik J. Linstead. Content type: Short ...