  The quality of a software is highly dependent on the quality of the tests it
is submitted to. Writing tests for bug detection is thus essential. However, it
is time-consuming when done manually. Automating test cases generation has
therefore been an exciting research area in the software engineering community.
Most approaches have been focused on generating unit tests. Unfortunately,
current efforts often do not lead to the generation of relevant inputs, which
limits the efficiency of automatically generated tests. Towards improving the
relevance of test inputs, we present \name, a technique for exploring bug
reports to identify input values that can be fed to automatic test generation
tools. In this work, we investigate the performance of using inputs extracted
from bug reports with \name to generate test cases with Evosuite. The
evaluation is performed on the Defects4J benchmark. For Defects4J projects, our
study has shown that \name successfully extracted 68.68\% of relevant inputs
when using regular expression in its approach versus 50.21\% relevant inputs
without regular expression. Further, our study has shown the potential to
improve the Line and Instruction Coverage across all projects. Overall, we
successfully collected relevant inputs that led to the detection of 45 bugs
that were previously undetected by the baseline.
