# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1703.05584