Estimación del Esfuerzo de Desarrollo de Software basado en la Combinación de Casificadores y Líneas de Código
(Software Development Effort Estimation based-on multiple classifier system and Lines of Code)
Héctor Velarde (email@example.com)1, Cosme Santiesteban (firstname.lastname@example.org)2, Ana Garcia (email@example.com)3, Jorge Casillas (firstname.lastname@example.org)4
1Universidad Católica de Santa María de Arequipa2Centro de Bioplantas. Universidad de Ciego de Avila3División Territorial Villa Clara, DESOFT4Universidad de Granada
This paper appears in: Revista IEEE América Latina
Publication Date: Aug. 2016
Volume: 14, Issue: 8
The development effort estimation is one of the most difficult problems in software project management. It is one of the most critical aspects in the early stages of the software project. Several software development effort estimation models have been proposed, however, these models are not able to obtain more than a 25 percent of accuracy, neither provide an understandable model for experts in the application area. Therefore, in this paper, we present EEpred, an explanatory model to estimate the development effort based on data of known software projects. It is a serial multiple classifier system based-on several decision trees. The model performance was evaluated by an internal validation procedure, analyzing their robustness and predictive performance. This procedure demonstrates that EEpred is able to estimate the software development effort with a 71 percent of precision. The main advantage of EEpred, regarding to other algorithms, is its ability to translate the process into a collection of simple decision rules, providing more easily interpretable knowledge that can help software engineer to improve decision-making on development planning.
Explanatory models, Effort estimation, Multiple Classifiers, Decision Trees, Regression Trees, Software metrics, Software projects
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