  Comments play an important role in updating Stack Overflow (SO) posts. They
are used to point out a problem (e.g., obsolete answer and buggy code) in a SO
answer or ask for more details about a proposed answer. We refer to this type
of comment as update request comments (URCs), which may trigger an update to
the answer post and thus improve its quality.
  In this study, we manually analyze a set of 384 sampled SO answer posts and
their associated 1,221 comments to investigate the prevalence of URCs and how
URCs are addressed. We find that around half of the analyzed comments are URCs.
While 55.3% of URCs are addressed within 24 hours, 36.5% of URCs remain
unaddressed after a year. Moreover, we find that the current community-vote
mechanism could not differentiate URCs from non-URCs. Thus many URCs might not
be aware by users who can address the issue or improve the answer quality. As a
first step to enhance the awareness of URCs and support future research on
URCs, we investigate the feasibility of URC detection by proposing a set of
features extracted from different aspects of SO comments and using them to
build supervised classifiers that can automatically identify URCs. Our
experiments on 377 and 289 comments posted on answers to JavaScript and Python
questions show that the proposed URC classifier can achieve an accuracy of 90%
and an AUC of 0.96, on average.
