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Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach.

Ch Anwar Ul Hassan1, Muhammad Sufyan Khan2, Rizwana Irfan3

  • 1Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan.

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This study enhances software cost estimation by optimizing COCOMO II coefficients and deep learning training using the HACO-BA algorithm. The hybrid approach significantly improves accuracy and reduces training time for deep neural networks.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • Software cost estimation is crucial for project decision-making.
  • Traditional COCOMO models have limitations due to fixed coefficients.
  • Deep learning (DL) offers potential but faces challenges like training delays and overfitting.

Purpose of the Study:

  • To fine-tune COCOMO II coefficients for improved accuracy.
  • To optimize the deep learning training process using meta-heuristic algorithms.
  • To evaluate the efficacy of a hybrid optimization technique (HACO-BA).

Main Methods:

  • Comparison of Ant Colony Optimization (ACO), BAT algorithm (BA), and a hybrid HACO-BA for COCOMO II coefficient optimization.
  • Application of HACO-BA to optimize deep neural network (DNN) training.
  • Experimental evaluation of accuracy and execution time.

Main Results:

  • The HACO-BA algorithm demonstrated superior performance in tuning COCOMO II coefficients compared to ACO and BA.
  • HACO-BA significantly improved DNN training, reducing execution time and enhancing accuracy.
  • The proposed DNN approach achieved approximately 98% accuracy, outperforming standard Neural Networks (NN) at 85%.

Conclusions:

  • The hybrid HACO-BA algorithm is effective for optimizing both COCOMO II coefficients and deep learning models.
  • This approach offers a promising solution for accurate and efficient software cost estimation.
  • The study highlights the potential of meta-heuristic algorithms in conjunction with deep learning for complex optimization tasks.