In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques.
- DOI
- 10.1371/journal.pone.0324847
- Published
- 2025
- Container
- PloS one
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0324847,
title = {In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques.},
author = {Ullah S and Uddin MI and Adnan M and Alarood AA and Alsulami A and Habibullah S},
year = {2025},
journal = {PloS one},
doi = {10.1371/journal.pone.0324847},
url = {https://doi.org/10.1371/journal.pone.0324847}
}RIS
TY - JOUR TI - In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques. AU - Ullah S AU - Uddin MI AU - Adnan M AU - Alarood AA AU - Alsulami A AU - Habibullah S PY - 2025 JO - PloS one DO - 10.1371/journal.pone.0324847 UR - https://doi.org/10.1371/journal.pone.0324847 ER -
APA
S, U., MI, U., M, A., AA, A., A, A., & S, H. (2025). In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques.. PloS one. https://doi.org/10.1371/journal.pone.0324847
Source records
- pubmed · retrieved 2026-09-25T08:09:52.040Z