RefactoCNN-system: an optimized deep learning framework for predicting software refactoring opportunities using CNN-based code analysis.

Prasanna EL, Srinivas K

DOI: 10.1038/s41598-026-56911-w
Published: 2026 Jun 9
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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BibTeX

@article{allodium:10.1038/s41598-026-56911-w,
  title = {RefactoCNN-system: an optimized deep learning framework for predicting software refactoring opportunities using CNN-based code analysis.},
  author = {Prasanna EL and Srinivas K},
  year = {2026},
  journal = {Scientific reports},
  doi = {10.1038/s41598-026-56911-w},
  url = {https://doi.org/10.1038/s41598-026-56911-w}
}

RIS

TY  - JOUR
TI  - RefactoCNN-system: an optimized deep learning framework for predicting software refactoring opportunities using CNN-based code analysis.
AU  - Prasanna EL
AU  - Srinivas K
PY  - 2026
JO  - Scientific reports
DO  - 10.1038/s41598-026-56911-w
UR  - https://doi.org/10.1038/s41598-026-56911-w
ER  -

APA

EL, P., & K, S. (2026). RefactoCNN-system: an optimized deep learning framework for predicting software refactoring opportunities using CNN-based code analysis.. Scientific reports. https://doi.org/10.1038/s41598-026-56911-w

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pubmed · retrieved 2026-09-25T15:30:21.581Z