Pair programming practices usually take place on a single computer where two programmers collaborate, taking on the roles of driver and observer. This article presents COLLECE, a groupware system to support synchronous distributed pair programming practices, and includes a variety of experimental activities in which two groups of programmers were involved to solve various programming problems. The first group carried out the activities by employing distributed pair programming practices using COLLECE, while the second group of solo programmers followed traditional programming practices. These experimental activities were used to conduct comparative studies of work productivity and program quality.

Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c’s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of $83\%$ indicates the potential of the proposed methodology.