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Granular computing is a widely used computational paradigm nowadays. Particularly, within the rough set theory, granular computing plays a key role. In this paper, we propose a generic approach of rough sets, the granular extended multigranular sets (GEMS) for dealing with both mixed and incomplete information systems. Not only our proposal does use the traditional optimistic and pessimistic granulations with respect to single attributes, but also we introduce granulations with respect to attribute sets, as well as two new ways of granulating: the optimistic + pessimistic granulation and the pessimistic + optimistic granulation. In addition, we have developed a particular case of the proposed GEMS: the multigranular maximum similarity rough sets (MMSRS). We have proved some of the properties of the MMSRS, and we tested its effectiveness with respect to other existing granular rough sets models. The experimental results show the flexibility and the capabilities of the proposed model, while handling mixed and incomplete information systems. [ABSTRACT FROM AUTHOR]

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 as unrealistic time frames and lack 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 and t paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models. [ABSTRACT FROM AUTHOR]