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Related Experiment Videos

Performance evaluation of SFP models using ML/DL and feature selection via cost evaluation framework.

Sonika Rathi1, Sanjay Misra2, Lalita Bhanu Murthy Neti3

  • 1BITS Pilani, Hyderabad, India. p20220202@hyderabad.bits-pilani.ac.in.

Scientific Reports
|July 12, 2026
PubMed
Summary

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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This study compares Deep Learning (DL) and Machine Learning (ML) for Software Fault Prediction (SFP), finding DL models competitive. Feature selection and cost evaluation frameworks are key for efficient SFP, reducing testing costs in complex software systems.

Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Software systems complexity challenges fault prevention.
  • Existing Software Fault Prediction (SFP) research lacks extensive comparative analysis of Deep Learning (DL) and Machine Learning (ML) with cost evaluation.
  • The combined impact of feature selection (FS) and DL algorithms in SFP is underexplored.

Purpose of the Study:

  • To conduct a large-scale comparative analysis of DL and ML-based SFP models.
  • To evaluate the effectiveness of various FS techniques and the Synthetic Minority Oversampling Technique (SMOTE) in SFP.
  • To assess the cost-effectiveness of DL-based SFP models using a Cost Evaluation Framework (CEF) and their potential to reduce real-world testing costs.

Main Methods:

Keywords:
Cost evaluation frameworkDeep learningDeep neural networkEmbeddingFeature selectionMajority voting ensembleSoftware fault prediction

Related Experiment Videos

  • Trained 32 SFP models (8 state-of-the-art, 16 ML, 6 DL) on 54 open-source projects.
  • Employed 11 FS techniques and SMOTE to address feature and class redundancy.
  • Utilized Accuracy, Area Under the ROC Curve (AUC), and a novel CEF for performance evaluation.

    • Main Results:

    • Without SMOTE, ML models like AdaBoost, Random Forest, and Extremely Randomized Trees showed high median AUC (0.72-0.73).
    • With SMOTE, Extremely Randomized Trees, Random Forest, and Bagging Decision Tree achieved superior median AUC (0.93-0.94).
    • DL models, particularly DL5 with SMOTE, demonstrated competitive performance (AUC 0.82) and feature selection reduced dataset size by 70-75% without performance loss.
    • Cost analysis indicated DL-based SFP models are most beneficial for projects with below-average faulty class percentages (27.17%-49.48%).

    Conclusions:

    • DL-based SFP models are competitive with ML approaches, offering comparable predictive capabilities.
    • Effective feature selection and class balancing techniques like SMOTE are crucial for optimizing SFP model performance.
    • The proposed CEF demonstrates the practical utility of SFP models in reducing software testing costs, especially for projects with lower fault densities.