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Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish

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An evolutionary morphological approach for software development cost estimation.

Ricardo de A Araújo¹, Adriano L I Oliveira, Sergio Soares

¹Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil. raa@cin.ufpe.br

Neural Networks : the Official Journal of the International Neural Network Society

|May 8, 2012

Summary

This study introduces a novel evolutionary morphological approach for software development cost estimation (SDCE). The dilation-erosion perceptron (DEP) model, trained with a modified genetic algorithm (MGA), shows promising results in solving complex SDCE problems.

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Area of Science:

Computer Science
Artificial Intelligence
Software Engineering

Background:

Software Development Cost Estimation (SDCE) is a critical but challenging problem.
Traditional methods often struggle with the complexity and non-linear nature of SDCE.
Existing artificial neuron models face limitations in gradient estimation for morphological operators.

Purpose of the Study:

To propose an evolutionary morphological approach for SDCE.
To introduce a novel hybrid artificial neuron, the dilation-erosion perceptron (DEP).
To develop an evolutionary learning process, DEP(MGA), to overcome gradient estimation issues.

Main Methods:

Developed a dilation-erosion perceptron (DEP) based on mathematical morphology (MM) and complete lattice theory (CLT).
Implemented a modified genetic algorithm (MGA) for an evolutionary learning process (DEP(MGA)) to train the DEP model.
Conducted experimental analysis on five complex SDCE problems using three standard performance metrics.

Main Results:

The DEP model demonstrated good performance in solving complex software development cost estimation problems.
The evolutionary learning process (DEP(MGA)) effectively addressed the non-differentiability of morphological operators.
Experimental results validated the efficacy of the proposed hybrid approach.

Conclusions:

The evolutionary morphological approach, specifically the DEP model trained with MGA, offers a viable solution for SDCE.
This method provides an effective way to handle the complexities inherent in software cost estimation.
The DEP model shows potential for improving the accuracy and reliability of SDCE.