  Context: Software engineering researchers have undertaken many experiments
investigating the potential of software defect prediction algorithms.
Unfortunately, some widely used performance metrics are known to be
problematic, most notably F1, but nevertheless F1 is widely used.
  Objective: To investigate the potential impact of using F1 on the validity of
this large body of research.
  Method: We undertook a systematic review to locate relevant experiments and
then extract all pairwise comparisons of defect prediction performance using F1
and the un-biased Matthews correlation coefficient (MCC).
  Results: We found a total of 38 primary studies. These contain 12,471 pairs
of results. Of these, 21.95% changed direction when the MCC metric is used
instead of the biased F1 metric. Unfortunately, we also found evidence
suggesting that F1 remains widely used in software defect prediction research.
  Conclusions: We reiterate the concerns of statisticians that the F1 is a
problematic metric outside of an information retrieval context, since we are
concerned about both classes (defect-prone and not defect-prone units). This
inappropriate usage has led to a substantial number (more than one fifth) of
erroneous (in terms of direction) results. Therefore we urge researchers to (i)
use an unbiased metric and (ii) publish detailed results including confusion
matrices such that alternative analyses become possible.
