Software Development Effort Estimation Using Regression Fuzzy Models

TL;DR

This paper compares three fuzzy logic models, enhanced with regression analysis, for estimating software development effort, highlighting the superior performance of the Sugeno linear model on industrial datasets.

Contribution

It introduces regression fuzzy logic models for effort estimation and systematically compares their performance, emphasizing the effectiveness of Sugeno linear models.

Findings

Sugeno linear model outperformed others

Fuzzy models are sensitive to outliers

Data heteroscedasticity affects model accuracy

Abstract

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.