Automatic detection of technical debt in large-scale java codebases: a multi-model deep learning methodology for enhanced software quality

Kategoria artykułu: Research Article

Data publikacji: 25 mar 2025

Otrzymano: 10 sty 2025

DOI: https://doi.org/10.2478/ijssis-2025-0012

Słowa kluczowe

technical debt, self-admitted technical debt, machine learning, random forest, gradient boosting, long short-term memory, gated recurrent unit, Java codebases, software engineering, automated detection

© 2025 Dr. Pooja Bagane et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.