In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques
- DOI
- 10.1371/journal.pone.0324847
- Published
- 2025-06-11
- Container
- PLOS One
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
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 = {Sajid Ullah and M. Irfan Uddin and Muhammad Adnan and Ala Abdulsalam Alarood and Abdulkream Alsulami and Safa Habibullah},
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 - Sajid Ullah AU - M. Irfan Uddin AU - Muhammad Adnan AU - Ala Abdulsalam Alarood AU - Abdulkream Alsulami AU - Safa Habibullah PY - 2025 JO - PLOS One DO - 10.1371/journal.pone.0324847 UR - https://doi.org/10.1371/journal.pone.0324847 ER -
APA
Ullah, S., Uddin, M. I., Adnan, M., Alarood, A. A., Alsulami, A., & Habibullah, S. (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
- crossref · retrieved 2026-11-07T15:48:19.716Z