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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

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

Figures & Tables

Figure 1:

TD prediction workflow. TD, technical debt.

Figure 2:

Frontend interface of the project. TD, technical debt.

Comparison of validation metrics

ModelMSERMSE
LSTM0.0033570.057947
GRU0.0054030.073505
RF regressor0.0060930.078061
GB regressor0.0079000.08888
DOI: https://doi.org/10.2478/ijssis-2025-0012 | Journal eISSN: 1178-5608
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Language: English
Submitted on: Jan 10, 2025
|
Published on: Mar 25, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
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
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2025 Pooja Bagane, Chahak Sengar, Sumedh Dongre, Siddharth Prabhakar, Obsa Amenu Jebessa, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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