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Enhancing analogy-based software cost estimation using Grey Wolf Optimization algorithm.

Taghi Javdani Gandomani1, Maedeh Dashti1, Sadegh Ansaripour2

  • 1Department of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Chaharmahal and Bakhtiari, Iran.

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Summary
This summary is machine-generated.

This study enhances software cost estimation (SCE) by integrating the Grey Wolf Optimization (GWO) algorithm with analogy-based estimation (ABE). The GWO-ABE method improves accuracy and robustness in predicting project costs, offering a more reliable solution for software development planning.

Keywords:
Analogy-based estimationGrey Wolf Optimization algorithmSoftware cost estimation

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Accurate software cost estimation (SCE) is crucial for project success, yet many projects exceed budget.
  • Analogy-based estimation (ABE) is a popular SCE method, but its accuracy can be improved.
  • Optimization algorithms offer potential to enhance ABE's precision in software size and cost estimation.

Purpose of the Study:

  • To present an innovative approach for SCE by integrating the Grey Wolf Optimization (GWO) algorithm with ABE.
  • To enhance the precision of ABE by optimizing its similarity function using GWO.
  • To evaluate the effectiveness of the proposed GWO-based ABE (GWO-ABE) method for software cost prediction.

Main Methods:

  • Mathematical modeling and incorporation of the GWO algorithm into the ABE framework.
  • Optimization of the ABE similarity function using Euclidean and Manhattan distance measures, with feature weighting.
  • Rigorous evaluation of GWO-ABE on multiple software project datasets using cross-validation and comparison with other evolutionary algorithms.

Main Results:

  • The GWO-ABE method demonstrates enhanced estimation accuracy and model robustness compared to traditional ABE.
  • GWO-ABE achieved notable improvements in key performance metrics, including reduced Mean Magnitude of Relative Error (MMRE) and Median Magnitude of Relative Error (MdMRE).
  • The method resulted in a higher Percentage of Prediction (PRED), indicating superior predictive performance across multiple datasets, especially with the Euclidean distance function.

Conclusions:

  • Integrating GWO into ABE effectively addresses limitations of traditional analogy-based methods by optimizing feature weights.
  • The proposed GWO-ABE approach offers a reliable and superior solution for software project cost estimation.
  • Metaheuristic optimization, exemplified by GWO, plays a significant role in advancing software estimation techniques for better project planning and management.