  Performance metrics are a core component of the evaluation of any machine
learning model and used to compare models and estimate their usefulness. Recent
work started to question the validity of many performance metrics for this
purpose in the context of software defect prediction. Within this study, we
explore the relationship between performance metrics and the cost saving
potential of defect prediction models. We study whether performance metrics are
suitable proxies to evaluate the cost saving capabilities and derive a theory
for the relationship between performance metrics and cost saving potential.
