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

Priya Varshini A G1, Anitha Kumari K2, Ramakrishnan S3

  • 1Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Coimbatore, Tamilnadu, India. priyavarshini.a.g@gmail.com.

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Summary

This study enhances software effort estimation using an Improved Adaptive Random Forest (IARF) model. The IARF model significantly boosts prediction accuracy and trustworthiness in software development projects.

Keywords:
Adaptive decision techniquesAlgorithm evaluationExplainable AI techniquesFeature importanceRandom forestSoftware effort estimation

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Area of Science:

  • Computer Science
  • Software Engineering
  • Machine Learning

Background:

  • Software Effort Estimation (SEE) is crucial for project management, resource allocation, and planning.
  • Achieving precise SEE predictions remains a challenge in the software industry.
  • Machine learning algorithms, particularly Random Forest (RF), show promise for improving SEE accuracy.

Purpose of the Study:

  • To enhance the accuracy and interpretability of software effort estimation models.
  • To introduce an Improved Adaptive Random Forest (IARF) model by integrating advanced techniques.
  • To provide more trustworthy decision-making in software development projects.

Main Methods:

  • Implemented an Improved Random Forest (IRF) with residual analysis, partial dependence plots, and feature engineering.
  • Extended IRF to an adaptive model (IARF) using Bayesian Optimization with Deep Kernel Learning (BO-DKL) for hyperparameter tuning.
  • Integrated Time-Series Residual Analysis for autocorrelation detection and Explainable AI (SHAP, LIME) for feature interpretability.

Main Results:

  • The IARF model demonstrated significant improvements across various metrics compared to the standard Random Forest (RF) model.
  • Average improvement ratios included: 18.5% on Mean Absolute Error (MAE), 20.3% on Root Mean Square Error (RMSE), 3.8% on R-Squared, and 5.4% on Mean Absolute Percentage Error (MAPE).
  • A 7% reduction in Mean Absolute Scaled Error (MASE) and a 3-5% improvement in Prediction Interval Coverage Probability (PICP) were also observed.

Conclusions:

  • The developed Improved Adaptive Random Forest (IARF) model substantially enhances software effort estimation accuracy.
  • The combination of adaptive learning and explainability techniques leads to more reliable predictions.
  • The findings support more trustworthy decision-making in software development project management.