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

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

International Journal on Smart Sensing and Intelligent Systems

Volume 18 (2025): Issue 1 (January 2025)

By: Pooja Bagane, Chahak Sengar, Sumedh Dongre, Siddharth Prabhakar and Obsa Amenu Jebessa

Open Access

|Mar 2025

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DOI: https://doi.org/10.2478/ijssis-2025-0012 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Jan 10, 2025

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Published on: Mar 25, 2025

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

technical debt,

self-admitted technical debt,

machine learning,

gradient boosting,

long short-term memory,

gated recurrent unit,

Java codebases,

software engineering,

automated detection

Related subjects:

Introductions and overviews,

Engineering, other

© 2025 Pooja Bagane, Chahak Sengar, Sumedh Dongre, Siddharth Prabhakar, Obsa Amenu Jebessa, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 18 (2025): Issue 1 (January 2025)

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