In any sufficiently complex software system there are experts, having a
deeper understanding of parts of the system than others. However, it is not
always clear who these experts are and which particular parts of the system
they can provide help with. We propose a framework to elicit the expertise of
developers and recommend experts by analyzing complexity measures over time.
Furthermore, teams can detect those parts of the software for which currently
no, or only few experts exist and take preventive actions to keep the
collective code knowledge and ownership high. We employed the developed
approach at a medium-sized company. The results were evaluated with a survey,
comparing the perceived and the computed expertise of developers. We show that
aggregated code metrics can be used to identify experts for different software
components. The identified experts were rated as acceptable candidates by
developers in over 90% of all cases