⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

February 24, 2026PLoS ONEOpen Access

Correction: A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI

Mohd Mustaqeem Suhel Mustajab Mahfooz Alam Fathe Jeribi Md Shadab Alam Mohammed Shuaib

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to improve software defect prediction through a reliable hybrid model while ensuring its transparency.
Correction of previous article regarding the hybrid model and defect prediction.
Emphasis on the integration of explainable AI (XAI) techniques.
Review of methodologies used in the original study.
Validates that the hybrid model enhances predictability of software defects.
Affirms that the model maintains transparency in its predictions.

No abstract yet

We only have this paper's title and publication details.

Read it at the source→

Implication

Correction reports a trustworthy hybrid model that predicts software defects, highlighting its transparency.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Mustaqeem et al. (2026) studied this question.

synapsesocial.com/papers/69a134b8ed1d949a99abe353 https://doi.org/10.1371/journal.pone.0343580

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI2024 · 18 citations
2A hybrid‐ensemble model for software defect prediction for balanced and imbalanced datasets using AI‐based techniques with feature preservation: SMERKP‐XGB2024 · 1 citations
3HYBRID TECHNIQUE FOR SOFTWARE DEFECT PREDICTION USING MACHINE LEARNING TECHNIQUES2025
4Hybrid deep learning for predicting software defects in agile environmentsOriginal Research · 2026
5Risk-Based Software Test Prioritization via Machine-Learning Defect Prediction: Label-Feature Circularity, Clean Baselines, and an Honest Empirical Study on Five Open-Source RepositoriesReview · 2026

Risk-Based Software Test Prioritization via Machine-Learning Defect Prediction: Label-Feature Circularity, Clean Baselines, and an Honest Empirical Study on Five Open-Source Repositories2026
Quantum-Inspired Deep Learning for Automated Software Defect Prediction: A Hybrid QLSTM-GCN Approach2026
An Explainable AI Framework for Bug Prediction and Pedagogical Feedback in Student Code2026
Acoustic measures of speech sound production in multilingual children: A scoping review protocolPLoS ONE · 2026
The diminishing returns of distance: Computational analysis of approach-avoidance behavior under threat of predationPLoS ONE · 2026

More from PLoS ONE