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Software Development Effort Estimation Using Regression Fuzzy Models.

Ali Bou Nassif1,2, Mohammad Azzeh3, Ali Idri4

  • 1Department of Electrical and Computer Engineering, University of Sharjah, P.O. Box 27272, Sharjah, UAE.

Computational Intelligence and Neuroscience
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This study compares three fuzzy logic models for software effort estimation. The Sugeno fuzzy inference system with linear output, designed using regression analysis, proved most effective for predicting software development effort.

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Artificial Intelligence

Background:

  • Accurate software effort estimation is crucial for project management, as errors can severely impact resources.
  • Machine learning, particularly fuzzy logic, is increasingly applied to handle imprecise data in software estimation.
  • Existing fuzzy logic models require careful design to optimize performance in software effort prediction.

Purpose of the Study:

  • To design and compare three distinct fuzzy logic models for software effort estimation: Mamdani, Sugeno (constant output), and Sugeno (linear output).
  • To investigate the utility of regression analysis in designing fuzzy logic models for software estimation, termed 'regression fuzzy logic'.

Main Methods:

  • Developed and evaluated three fuzzy logic models (Mamdani, Sugeno with constant output, Sugeno with linear output) for software effort estimation.
  • Employed regression analysis to aid in the design of the fuzzy logic models.
  • Utilized unbiased performance metrics (standardized accuracy, effect size, mean balanced relative error) and statistical tests for model evaluation.
  • Trained and tested models using the International Software Benchmarking Standards Group (ISBSG) dataset of industrial projects.

Main Results:

  • Data heteroscedasticity was observed to significantly influence the performance of the fuzzy logic models.
  • Fuzzy logic models demonstrated high sensitivity to outliers in the dataset.
  • The Sugeno fuzzy inference system with linear output, when designed using regression analysis, exhibited superior performance compared to the other models.

Conclusions:

  • The Sugeno fuzzy inference system with linear output, enhanced by regression analysis, is a highly effective approach for software effort estimation.
  • Fuzzy logic models require careful consideration of data characteristics like heteroscedasticity and outliers for optimal application.
  • Regression fuzzy logic offers a promising methodology for improving the accuracy of software effort prediction models.