  This paper explores the concepts of modelling a software development project
as a process that results in the creation of a continuous stream of data. In
terms of the Jazz repository used in this research, one aspect of that stream
of data would be developer communication. Such data can be used to create an
evolving social network characterized by a range of metrics. This paper
presents the application of data stream mining techniques to identify the most
useful metrics for predicting build outcomes. Results are presented from
applying the Hoeffding Tree classification method used in conjunction with the
Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results
indicate that only a small number of the available metrics considered have any
significance for predicting the outcome of a build.
