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<p>Software Effort estimation (SEE) is a vital task for project management as it is essential for resource allocation and project planning. Numerous algorithms have been investigated for forecasting software effort, yet achieving precise predictions remains a significant hurdle in the software industry. To achieve optimal accuracy, machine learning algorithms are employed. Remarkably, Random Forest (RF) algorithm produced better accuracy when compared with various algorithms. In this paper, the prediction is extended by increasing the number of trees and Improved Random Forest (IRF) is implemented by including three decision techniques such as residual analysis, partial dependence plots and feature engineering to improve prediction accuracy. To make improved random forest to be adaptive, it is further extended in this paper by integrating three techniques such as: Bayesian Optimization with Deep Kernel Learning (BO-DKL) to adaptively set hyperparameters, Time-Series Residual Analysis to detect autocorrelation patterns among model error, and Explainable AI techniques Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to improve feature interpretability. This Improved Adaptive Random Forest (IARF) mutually contributes to a comprehensive evaluation and improvement of accuracy in prediction. Metrics used for evaluation are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared, Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE) and Prediction Interval Coverage Probability (PICP). Overall, the improved adaptive RF model had an average improvement ratio of 18.5% on MAE, 20.3% on RMSE, 3.8% on R<sup>2</sup>, 5.4% on MAPE, 7% reduction in MASE and a 3–5% improvement in PICP across all data sets compared to the Random Forest model, with much improved prediction accuracy. These findings validate that the combination of adaptive learning methods and explainability-based adjustments considerably improves accuracy of software effort estimation models and facilitates more trustworthy decision-making in software development projects.</p>

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Enhancing software effort estimation with random forest tuning and adaptive decision strategies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Priya Varshini A G,
  • Anitha Kumari K,
  • Ramakrishnan S

摘要

Software Effort estimation (SEE) is a vital task for project management as it is essential for resource allocation and project planning. Numerous algorithms have been investigated for forecasting software effort, yet achieving precise predictions remains a significant hurdle in the software industry. To achieve optimal accuracy, machine learning algorithms are employed. Remarkably, Random Forest (RF) algorithm produced better accuracy when compared with various algorithms. In this paper, the prediction is extended by increasing the number of trees and Improved Random Forest (IRF) is implemented by including three decision techniques such as residual analysis, partial dependence plots and feature engineering to improve prediction accuracy. To make improved random forest to be adaptive, it is further extended in this paper by integrating three techniques such as: Bayesian Optimization with Deep Kernel Learning (BO-DKL) to adaptively set hyperparameters, Time-Series Residual Analysis to detect autocorrelation patterns among model error, and Explainable AI techniques Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to improve feature interpretability. This Improved Adaptive Random Forest (IARF) mutually contributes to a comprehensive evaluation and improvement of accuracy in prediction. Metrics used for evaluation are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared, Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE) and Prediction Interval Coverage Probability (PICP). Overall, the improved adaptive RF model had an average improvement ratio of 18.5% on MAE, 20.3% on RMSE, 3.8% on R2, 5.4% on MAPE, 7% reduction in MASE and a 3–5% improvement in PICP across all data sets compared to the Random Forest model, with much improved prediction accuracy. These findings validate that the combination of adaptive learning methods and explainability-based adjustments considerably improves accuracy of software effort estimation models and facilitates more trustworthy decision-making in software development projects.