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LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model.

Maedeh Dashti1, Taghi Javdani Gandomani1,2, Dariush Hasanpoor Adeh3

  • 1Data Science Research Center, Shahrekord University, Shahrekord, Chaharmahal and Bakhtiari, Iran.

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Summary
This summary is machine-generated.

Accurate software cost estimation is crucial for project success. This study introduces a new method using the LEM algorithm to optimize feature weighting, significantly improving estimation accuracy on benchmark datasets.

Keywords:
Analogy-based estimationFeatures weighting optimizationLearnable evolution model (LEM)Software cost estimationSoftware cost estimation frameworkSoftware project management

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Area of Science:

  • Software Engineering
  • Project Management
  • Computational Intelligence

Background:

  • Accurate software cost estimation is vital but challenging, especially in early project stages.
  • Analogy-based estimation is a popular approach, yet improving its accuracy remains an active research area.
  • Existing techniques often struggle to achieve optimal estimation precision.

Purpose of the Study:

  • To enhance software development cost estimation accuracy.
  • To investigate the impact of the LEM algorithm on feature weighting optimization.
  • To propose and evaluate a novel cost estimation method.

Main Methods:

  • The study employed the LEM (Learning, Evaluating, and Mapping) algorithm for feature weighting optimization.
  • A new analogy-based cost estimation method was developed and implemented.
  • The proposed method was validated using the Desharnais and Maxwell datasets.

Main Results:

  • The proposed method demonstrated considerable improvements in software cost estimation.
  • Evaluation using MMRE, PRED (0.25), and MdMRE criteria showed superior performance compared to other evolutionary algorithms.
  • The LEM algorithm effectively optimized feature weights, leading to enhanced estimation accuracy.

Conclusions:

  • The integration of the LEM algorithm offers a promising approach to optimize feature weighting in analogy-based software cost estimation.
  • The proposed method provides a significant advancement in achieving more accurate software cost predictions.
  • Further research can explore the application of this method on diverse software project datasets.