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Prediction accuracy measurements as a fitness function for software effort estimation.

Tomas Urbanek¹, Zdenka Prokopova¹, Radek Silhavy¹

¹Department of Computer and Comunication systems, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin, Czech Republic.

|December 24, 2015

Summary

This study recommends Mean Squared Error (MSE) as the best fitness function for software effort estimation using analytical programming and differential evolution. Analytical programming also proves effective for calibrating the use case points method.

Keywords:

Analytical programming Differential evolution Effort estimation Prediction accuracy measures Software engineering Use case points

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Area of Science:

Computer Science
Software Engineering
Artificial Intelligence

Background:

Software effort estimation is crucial for project management.
Differential evolution requires appropriate fitness functions for optimization.
Selecting the right fitness function is a key challenge.

Purpose of the Study:

To evaluate analytical programming and various fitness functions for software effort estimation.
To identify the most effective fitness function for differential evolution in this context.
To assess the viability of analytical programming for calibrating the use case points method.

Main Methods:

Analytical programming and differential evolution were used to generate regression functions.
Several metrics, including Mean Squared Error (MSE), were tested as fitness functions.
Experimental results were validated through visual inspection and statistical significance testing.

Main Results:

Mean Squared Error (MSE) demonstrated superior performance compared to other tested fitness functions.
Analytical programming was found to be a viable method for calibrating the use case points method.
The study provides insights into the selection of fitness functions for effective optimization.

Conclusions:

MSE is recommended as the optimal fitness function for software effort estimation using analytical programming and differential evolution.
Analytical programming offers a practical approach for enhancing the use case points method.
This research contributes to more accurate and reliable software development effort predictions.