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Related Experiment Videos

An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting.

Qi Kun1, Zaffar Ahmed Shaikh2,3, Jing Yang4

  • 1School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, Guangdong, China.

Scientific Reports
|June 3, 2026
PubMed
Summary

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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This summary is machine-generated.

This study introduces a Transformer Assisted Boosting Framework (TABF) for accurate software defect prediction. TABF enhances model interpretability and outperforms traditional machine learning methods, improving software quality assurance.

Area of Science:

  • Software Engineering
  • Machine Learning
  • Artificial Intelligence

Background:

  • Accurate software defect prediction is crucial for mitigating project delays, cost overruns, and reliability issues.
  • Existing machine learning models often lack interpretability, hindering practical application in software quality assurance.

Purpose of the Study:

  • To develop and evaluate a novel Transformer Assisted Boosting Framework (TABF) for enhanced software defect prediction.
  • To combine the predictive power of XGBoost with the interpretability of Transformer self-attention mechanisms.
  • To improve the accuracy and explainability of defect prediction models for practical use.

Main Methods:

  • The Transformer Assisted Boosting Framework (TABF) integrates XGBoost with Transformer self-attention.
Keywords:
Decision makingExplainable analyticsMachine learningProject planningSoftware defect predictionSoftware project managementSoftware quality assurance

Related Experiment Videos

  • Evaluation was performed using the NASA Metrics Data Program (MDP) and Code4Code datasets.
  • Key software metrics include cyclomatic complexity, Halstead's properties, and lines of code.
  • SHapley Additive exPlanations (SHAP) were employed for feature importance analysis.

    • Main Results:

    • TABF achieved superior performance with AUC scores of 0.95 and ROC of 0.96.
    • The framework outperformed classical models like Random Forest (92.5% accuracy) and SVM (94.3% accuracy).
    • Lines of code and McCabe's cyclomatic complexity were identified as key defect predictors via SHAP analysis.

    Conclusions:

    • TABF offers a powerful and interpretable approach to software defect prediction.
    • The framework bridges the gap between advanced ML/DL models and practical software quality assurance.
    • Insights from TABF aid in defect management, resource allocation, and enhancing overall software reliability.