TY - JOUR
T1 - Particle swarm optimization for predicting the development effort of software projects
AU - Alanis-Tamez, Mariana Dayanara
AU - López-Martín, Cuauhtémoc
AU - Villuendas-Rey, Yenny
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. T.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - ISBSG
KW - Particle swarm optimization
KW - Software development effort prediction
KW - Software project planning
UR - http://www.scopus.com/inward/record.url?scp=85093080928&partnerID=8YFLogxK
U2 - 10.3390/math8101819
DO - 10.3390/math8101819
M3 - Artículo
AN - SCOPUS:85093080928
SN - 2227-7390
VL - 8
SP - 1
EP - 21
JO - Mathematics
JF - Mathematics
IS - 10
M1 - 1819
ER -