3 research outputs found
Software Development Effort Estimation Using Regression Fuzzy Models
Software effort estimation plays a critical role in project management.
Erroneous results may lead to overestimating or underestimating effort, which
can have catastrophic consequences on project resources. Machine-learning
techniques are increasingly popular in the field. Fuzzy logic models, in
particular, are widely used to deal with imprecise and inaccurate data. The
main goal of this research was to design and compare three different fuzzy
logic models for predicting software estimation effort: Mamdani, Sugeno with
constant output and Sugeno with linear output. To assist in the design of the
fuzzy logic models, we conducted regression analysis, an approach we call
regression fuzzy logic. State-of-the-art and unbiased performance evaluation
criteria such as standardized accuracy, effect size and mean balanced relative
error were used to evaluate the models, as well as statistical tests. Models
were trained and tested using industrial projects from the International
Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data
heteroscedasticity affected model performance. Fuzzy logic models were found to
be very sensitive to outliers. We concluded that when regression analysis was
used to design the model, the Sugeno fuzzy inference system with linear output
outperformed the other models.Comment: This paper has been accepted in January 2019 in Computational
Intelligence and Neuroscience Journal (In Press