Using Developer Information as a Factor for Fault Prediction

We have been investigating different prediction models to identify which files of a large multi-release industrial software system are most likely to contain the largest numbers of faults in the next release. To make predictions we considered a number of different file characteristics and change information about the files, and have built fully- automatable models that do not require that the user have any statistical expertise. We now consider the effect of adding developer information as a prediction factor and assess the extent to which this affects the quality of the predictions.