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In today’s software engineering landscape, accurately forecasting software reuse effectiveness is crucial for optimizing development workflows. This paper presents a comprehensive framework that leverages cutting-edge machine learning and ensemble techniques. By employing a diverse range of algorithms like Naive Bayes, ANN, Hoeffding Trees, SVM, and KNN, we capture various facets of the prediction problem. Ensemble learning, especially stacking, amalgamates multiple models to enhance predictive accuracy. The evaluation criteria considered are CCI, KS, MAE, TPR, FPR, Precision, Recall, and F-measure. Our integrated approach aims to provide actionable insights to both software developers and project managers to better navigate the complexities of software reuse and maximize the success of projects in the changing landscape of software engineering.

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Ensemble Learning Based Approach to Predict Effective Software Reuse

  • 会议论文

  • 发表日期：

    2025年

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  • Naman Chauhan,
  • Riya Tomar,
  • Pranshul Atri,
  • Chhavi Chauhan,
  • Yash Tomar

摘要

In today’s software engineering landscape, accurately forecasting software reuse effectiveness is crucial for optimizing development workflows. This paper presents a comprehensive framework that leverages cutting-edge machine learning and ensemble techniques. By employing a diverse range of algorithms like Naive Bayes, ANN, Hoeffding Trees, SVM, and KNN, we capture various facets of the prediction problem. Ensemble learning, especially stacking, amalgamates multiple models to enhance predictive accuracy. The evaluation criteria considered are CCI, KS, MAE, TPR, FPR, Precision, Recall, and F-measure. Our integrated approach aims to provide actionable insights to both software developers and project managers to better navigate the complexities of software reuse and maximize the success of projects in the changing landscape of software engineering.