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

Pooja Bagane

Pooja Bagane

Department of Computer Science and Engineering, Symbiosis Institute of Technology-Pune Campus, Symbiosis International (Deemed University)Pune, India
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Bagane, Pooja
, 
Chahak Sengar

Chahak Sengar

Department of Computer Science and Engineering, Symbiosis Institute of Technology-Pune Campus, Symbiosis International (Deemed University)Pune, India
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Sengar, Chahak
, 
Sumedh Dongre

Sumedh Dongre

Department of Computer Science and Engineering, Symbiosis Institute of Technology-Pune Campus, Symbiosis International (Deemed University)Pune, India
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Dongre, Sumedh
, 
Siddharth Prabhakar

Siddharth Prabhakar

Department of Computer Science and Engineering, Symbiosis Institute of Technology-Pune Campus, Symbiosis International (Deemed University)Pune, India
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Prabhakar, Siddharth
 y 
Obsa Amenu Jebessa

Obsa Amenu Jebessa

Department of Information Technology, Jimma Institute of TechnologyJimma, Ethiopia
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Jebessa, Obsa Amenu
  
25 mar 2025
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Categoría del artículo: Research Article
Publicado en línea: 25 mar 2025
Recibido: 10 ene 2025
DOI: https://doi.org/10.2478/ijssis-2025-0012
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© 2025 Dr. Pooja Bagane et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

TD prediction workflow. TD, technical debt.
TD prediction workflow. TD, technical debt.

Figure 2:

Frontend interface of the project. TD, technical debt.
Frontend interface of the project. TD, technical debt.

Comparison of validation metrics

Model MSE RMSE
LSTM 0.003357 0.057947
GRU 0.005403 0.073505
RF regressor 0.006093 0.078061
GB regressor 0.007900 0.08888
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