Next Article in Journal
Binary Whale Optimization Algorithm for Dimensionality Reduction
Previous Article in Journal
Double Cyclic Codes over \({\mathbb{F}_{q}+v\mathbb{F}_{q}}\)
Open AccessArticle

Particle Swarm Optimization for Predicting the Development Effort of Software Projects

1
Centro de Investigación en Computación, Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX, Mexico City 07700, Mexico
2
Oracle, Fusion Adaptative Intelligence, Paseo Valle Real 1275, Valle Real, Zapopan, Jal, Guadalajara 45136, Mexico
3
Department of Information Systems, Universidad de Guadalajara, Periférico Norte N° 799, Núcleo Universitario Los Belenes, Zapopan 45100, Jalisco, Mexico
4
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
*
Authors to whom correspondence should be addressed.
Mathematics 2020, 8(10), 1819; https://doi.org/10.3390/math8101819
Received: 22 September 2020 / Revised: 11 October 2020 / Accepted: 14 October 2020 / Published: 17 October 2020
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. View Full-Text
Keywords: software project planning; software development effort prediction; particle swarm optimization; ISBSG software project planning; software development effort prediction; particle swarm optimization; ISBSG
Show Figures

Figure 1

MDPI and ACS Style

Alanis-Tamez, M.D.; López-Martín, C.; Villuendas-Rey, Y. Particle Swarm Optimization for Predicting the Development Effort of Software Projects. Mathematics 2020, 8, 1819.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop