Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Ferreira-Santiago, Angel; López-Martín, Cuauhtémoc; Yáñez-Márquez, Cornelio
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2229
• Page End: 2240
• Publication Date: 2016-11
• Subjects: Software project schedule prediction -- Multivariate adaptive regression splines -- Statistical regression -- Simulated annealing -- ISBSG; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2003-z
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract A qualitative common perception of the software industry is that it finishes its projects late and over budget, whereas from a quantitative point of view, only 39 % of software projects are finished on time compared to the schedule when the project started. This low percentage has been attributed to factors such asunrealistic time frames andlack of planning regarding poor prediction. The main techniques used for predicting project schedule have mainly been based on expert judgment and mathematical models. In this study, a new model, derived from the multivariate adaptive regression splines (MARS) model, is proposed. This new model, optimized MARS (OMARS), uses a simulated annealing process to find a transformation of the input data space prior to applying MARS in order to improve accuracy when predicting the schedule of software projects. The prediction accuracy of the OMARS model is compared to that of stand-alone MARS and a multiple linear regression (MLR) model with a logarithmic transformation. The two independent variables used for training and testing the models are functional size, which corresponds to a composite value of 19 independent variables, and the maximum size of the team of developers. The data set of projects was obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11. Results based on the absolute residuals andt paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models.