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Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy.

Anum Yasmin1, Wasi Haider Butt1, Ali Daud2

  • 1Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.

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|April 4, 2024
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Summary
This summary is machine-generated.

Optimizing machine learning models with metaheuristic algorithms significantly improves software development effort estimation accuracy. This approach enhances ensemble models by fine-tuning hyperparameters and ensemble weights for better project resource management.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Accurate software development effort estimation (SDEE) is crucial for effective project management, as inaccuracies lead to resource mismanagement.
  • Machine learning (ML), particularly ensemble effort estimation (EEE), is widely used in SDEE to mitigate bias and subjectivity of individual ML models.
  • The performance of EEE heavily relies on hyperparameter settings and the weighting of individual models, areas with limited prior research on optimization.

Purpose of the Study:

  • To enhance SDEE performance by integrating metaheuristic optimization for hyperparameters and weight assignment within EEE models.
  • To introduce a novel approach, Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE), for improved accuracy and diversity in ensemble models.

Main Methods:

  • Proposed a Multi-dimensional bagging (Mdb) technique for search space division and hyperparameter optimization.
  • Utilized the Firefly Algorithm (FFA) to determine optimal hyperparameters for base ML algorithms (Random Forest, Support Vector Machine, Deep Neural Network).
  • Implemented FFA-based weight optimization to create a Metaheuristic-optimized weighted ensemble (MoWE) of individual Mdb schemes.

Main Results:

  • The MoMdbWE approach demonstrated significantly enhanced performance across eight effort estimation datasets.
  • Evaluations using MAE, RMSE, MMRE, MdMRE, Pred, accuracy, and effect size confirmed superior results compared to base algorithms and other EEE techniques.
  • Statistical analysis using the Wilcoxon signed-rank test validated the significant performance improvements achieved through FFA optimization.

Conclusions:

  • Metaheuristic optimization of hyperparameters and ensemble weights substantially improves SDEE performance.
  • The proposed MoMdbWE approach offers a robust method for enhancing the accuracy and reliability of software development effort estimation.
  • This research highlights the potential of advanced optimization techniques in addressing challenges within the SDEE domain.