Particle Swarm Optimization for Predicting the Development Effort of Software Projects

Mariana Dayanara Alanis-Tamez, Cuauhtémoc López-Martín and Yenny Villuendas-Rey
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Mariana Dayanara Alanis-Tamez: Centro de Investigación en Computación, Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX, Mexico City 07700, Mexico
Cuauhtémoc López-Martín: Department of Information Systems, Universidad de Guadalajara, Periférico Norte N° 799, Núcleo Universitario Los Belenes, Zapopan 45100, Jalisco, Mexico
Yenny Villuendas-Rey: Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX, Mexico City 07700, Mexico

Mathematics, 2020, vol. 8, issue 10, 1-21

Abstract: Software project planning includes as one of its main activities software development effort prediction (SDEP). Effort (measured in person-hours) is useful to budget and bidding the projects. It corresponds to one of the variables most predicted, actually, hundreds of studies on SDEP have been published. Therefore, we propose the application of the Particle Swarm Optimization (PSO) metaheuristic for optimizing the parameters of statistical regression equations (SRE) applied to SDEP. Our proposal incorporates two elements in PSO: the selection of the SDEP model, and the automatic adjustment of its parameters. The prediction accuracy of the SRE optimized through PSO ( PSO-SRE ) was compared to that of a SRE model. These models were trained and tested using eight data sets of new and enhancement software projects obtained from an international public repository of projects. Results based on statistically significance showed that the PSO-SRE was better than the SRE in six data sets at 99% of confidence, in one data set at 95%, and statistically equal than SRE in the remaining data set. We can conclude that the PSO can be used for optimizing SDEP equations taking into account the type of development, development platform, and programming language type of the projects.

Keywords: software project planning; software development effort prediction; particle swarm optimization; ISBSG (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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