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Context <p>Software Fault Prediction (SFP) leverages supervised Machine Learning to detect faulty software constructs using software metrics and corresponding labels. Despite recent advances in Deep Learning (DL) for tabular data, their application to SFP remains underexplored.</p> Objectives <p>This study proposes a novel feature selection method, CorrBoost, which combines correlation analysis and XGBoost to address feature dimensionality. Additionally, we evaluate existing tabular DL architectures, super convergent deep neural networks (sDNN) and TabNet for SFP.</p> Methods <p>Using 26 public datasets from NASA, PROMISE, and AEEEM repositories, we apply the adaptive synthetic oversampling technique to manage class imbalance. We compare DL models with five state-of-the-art techniques and two gradient-boosted tree models (XGBoost and LightGBM) using AUC-ROC, AUPRC, and Accuracy. Statistical significance is validated using the Bayesian Signed Rank Test and Scott-Knott ESD.</p> Results <p>Gradient-boosted trees and existing state-of-the-art models outperform DL methods in AUC-ROC by 17.9% and 9.6%, respectively. CorrBoost achieves a 55% average reduction in feature dimensionality with negligible performance loss. DL methods, however, incur significantly higher processing time and perform poorly on unseen test data.</p> Conclusion <p>CorrBoost combined with boosted tree models offers a superior trade-off between performance and computation. While tabular DL architectures hold promise, they currently lag behind traditional methods for SFP on real-world data.</p>

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CorrBoost: a feature selection technique and utility of tabular deep neural networks in software fault prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Tamanna Sharma,
  • Sanjay Misra,
  • Ricardo Colomo-Palacios

摘要

Context

Software Fault Prediction (SFP) leverages supervised Machine Learning to detect faulty software constructs using software metrics and corresponding labels. Despite recent advances in Deep Learning (DL) for tabular data, their application to SFP remains underexplored.

Objectives

This study proposes a novel feature selection method, CorrBoost, which combines correlation analysis and XGBoost to address feature dimensionality. Additionally, we evaluate existing tabular DL architectures, super convergent deep neural networks (sDNN) and TabNet for SFP.

Methods

Using 26 public datasets from NASA, PROMISE, and AEEEM repositories, we apply the adaptive synthetic oversampling technique to manage class imbalance. We compare DL models with five state-of-the-art techniques and two gradient-boosted tree models (XGBoost and LightGBM) using AUC-ROC, AUPRC, and Accuracy. Statistical significance is validated using the Bayesian Signed Rank Test and Scott-Knott ESD.

Results

Gradient-boosted trees and existing state-of-the-art models outperform DL methods in AUC-ROC by 17.9% and 9.6%, respectively. CorrBoost achieves a 55% average reduction in feature dimensionality with negligible performance loss. DL methods, however, incur significantly higher processing time and perform poorly on unseen test data.

Conclusion

CorrBoost combined with boosted tree models offers a superior trade-off between performance and computation. While tabular DL architectures hold promise, they currently lag behind traditional methods for SFP on real-world data.