Software Quality Assessment Using Explainable Machine Learning

  • Authors

    • Dr. Nandhini Ravi Assistant Professor VIT University, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022P2T9W

    Published 01-02-2022

  • Software Quality Assessment, Explainable Machine Learning, SHAP Values, LIME, Code Metrics, Software Defect Prediction, Software Reliability, Model Interpretability, Software Engineering Analytics, Quality Assurance

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    N. Ravi, “Software Quality Assessment Using Explainable Machine Learning”, IJMLPA, vol. 5, no. 1, pp. 01–09, Jan. 2022, doi: 10.67228/3142788X/IJMLPA-2022P2T9W.

  • Abstract

    Ensuring software quality is critical for the reliability, maintainability, and usability of modern software systems. Traditional software quality assessment techniques often rely on manual reviews, static analysis, or classical machine learning models that offer limited interpretability. This research proposes an Explainable Machine Learning (XML)–based framework to assess software quality by integrating code metrics, defect datasets, and advanced interpretability methods such as SHAP, LIME, and permutation importance. The study evaluates multiple ML models—Random Forest, Gradient Boosting, XGBoost, and Neural Networks—to predict software quality attributes including reliability, maintainability, and defect proneness. Explainability techniques are applied to interpret model decisions, identify key quality indicators, and provide insights useful for developers, testers, and project managers. Experimental results demonstrate that explainable ML improves both predictive performance and decision transparency, making it suitable for practical software engineering environments. This research highlights how combining ML with explainability techniques enhances trust, interpretability, and actionable insights in software quality assessment.

  • References

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