  Context: Addressing user requests in the form of bug reports and Github
issues represents a crucial task of any successful software project. However,
user-submitted issue reports tend to widely differ in their quality, and
developers spend a considerable amount of time handling them. Objective: By
collecting a dataset of around 6,000 issues of 279 GitHub projects, we observe
that developers take significant time (i.e., about five months, on average)
before labeling an issue as a wontfix. For this reason, in this paper, we
empirically investigate the nature of wontfix issues and methods to facilitate
issue management process. Method: We first manually analyze a sample of 667
wontfix issues, extracted from heterogeneous projects, investigating the common
reasons behind a "wontfix decision", the main characteristics of wontfix issues
and the potential factors that could be connected with the time to close them.
Furthermore, we experiment with approaches enabling the prediction of wontfix
issues by analyzing the titles and descriptions of reported issues when
submitted. Results and conclusion: Our investigation sheds some light on the
wontfix issues' characteristics, as well as the potential factors that may
affect the time required to make a "wontfix decision". Our results also
demonstrate that it is possible to perform prediction of wontfix issues with
high average values of precision, recall, and F-measure (90%-93%).
