Software effort estimation based on optimized model tree

TL;DR

This paper presents an optimized model tree approach for software effort estimation, utilizing the Bees algorithm to automatically select optimal parameters, resulting in improved prediction accuracy across multiple datasets.

Contribution

It introduces the use of the Bees algorithm to optimize model tree parameters specifically for software effort estimation, enhancing accuracy over existing methods.

Findings

Optimized model tree with Bees algorithm outperforms other estimation methods.

The approach improves prediction accuracy across diverse datasets.

Automated parameter tuning benefits effort estimation models.

Abstract

Background: It is widely recognized that software effort estimation is a regression problem. Model Tree (MT) is one of the Machine Learning based regression techniques that is useful for software effort estimation, but as other machine learning algorithms, the MT has a large space of configuration and requires to carefully setting its parameters. The choice of such parameters is a dataset dependent so no general guideline can govern this process which forms the motivation of this work. Aims: This study investigates the effect of using the most recent optimization algorithm called Bees algorithm to specify the optimal choice of MT parameters that fit a dataset and therefore improve prediction accuracy. Method: We used MT with optimal parameters identified by the Bees algorithm to construct software effort estimation model. The model has been validated over eight datasets come from two main sources: PROMISE and ISBSG. Also we used 3-Fold cross validation to empirically assess the prediction accuracies of different estimation models. As benchmark, results are also compared to those obtained with Stepwise Regression Case-Based Reasoning and Multi-Layer Perceptron. Results: The results obtained from combination of MT and Bees algorithm are encouraging and outperforms other well-known estimation methods applied on employed datasets. They are also interesting enough to suggest the effectiveness of MT among the techniques that are suitable for effort estimation. Conclusions: The use of the Bees algorithm enabled us to automatically find optimal MT parameters required to construct effort estimation models that fit each individual dataset. Also it provided a significant improvement on prediction accuracy.