  Being light-weight and cost-effective, IR-based approaches for bug
localization have shown promise in finding software bugs. However, the accuracy
of these approaches heavily depends on their used bug reports. A significant
number of bug reports contain only plain natural language texts. According to
existing studies, IR-based approaches cannot perform well when they use these
bug reports as search queries. On the other hand, there is a piece of recent
evidence that suggests that even these natural language-only reports contain
enough good keywords that could help localize the bugs successfully. On one
hand, these findings suggest that natural language-only bug reports might be a
sufficient source for good query keywords. On the other hand, they cast serious
doubt on the query selection practices in the IR-based bug localization. In
this article, we attempted to clear the sky on this aspect by conducting an
in-depth empirical study that critically examines the state-of-the-art query
selection practices in IR-based bug localization. In particular, we use a
dataset of 2,320 bug reports, employ ten existing approaches from the
literature, exploit the Genetic Algorithm-based approach to construct optimal,
near-optimal search queries from these bug reports, and then answer three
research questions. We confirmed that the state-of-the-art query construction
approaches are indeed not sufficient for constructing appropriate queries (for
bug localization) from certain natural language-only bug reports although they
contain such queries. We also demonstrate that optimal queries and non-optimal
queries chosen from bug report texts are significantly different in terms of
several keyword characteristics, which has led us to actionable insights.
Furthermore, we demonstrate 27%--34% improvement in the performance of
non-optimal queries through the application of our actionable insights to them.
