A genetic algorithm based framework for software effort prediction
Keywords:Software effort estimation, Machine learning, Effort prediction model, Genetic approach, Learning schemes, Function points, ISBSG dataset, Empirical study
Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the most critical and complex issues in the software industry. The automated selection and the combination of techniques in alternative ways could improve the overall accuracy of the prediction models.;Objectives
In this study, we validate an automated genetic framework, and then conduct a sensitivity analysis across different genetic configurations. Following is the comparison of the framework with a baseline random guessing and an exhaustive framework. Lastly, we investigate the performance results of the best learning schemes.;Methods
In total, six hundred learning schemes that include the combination of eight data preprocessors, five attribute selectors and fifteen modeling techniques represent our search space. The genetic framework, through the elitism technique, selects the best learning schemes automatically. The best learning scheme in this context means the combination of data preprocessing + attribute selection + learning algorithm with the highest coefficient correlation possible. The selected learning schemes are applied to eight datasets extracted from the ISBSG R12 Dataset.;
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Copyright (c) 2021 Juan Murillo-Morera, Christian Quesada-López, Carlos Castro-Herrera, Marcelo Jenkins
This work is licensed under a Creative Commons Attribution 4.0 International License.