  The performance of coverage-based fault localization greatly depends on the
quality of test cases being executed. These test cases execute some lines of
the given program and determine whether the underlying tests are passed or
failed. In particular, some test cases may be well-behaved (i.e., passed) while
executing faulty statements. These test cases, also known as coincidentally
correct test cases, may negatively influence the performance of the
spectra-based fault localization and thus be less helpful as a tool for the
purpose of automated debugging. In other words, the involvement of these
coincidentally correct test cases may introduce noises to the fault
localization computation and thus cause in divergence of effectively localizing
the location of possible bugs in the given code. In this paper, we propose a
hybrid approach of ensemble learning combined with a supervised learning
algorithm namely, Random Forests (RF) for the purpose of correctly identifying
test cases that are mislabeled to be the passing test cases. A cost-effective
analysis of flipping the test status or trimming (i.e., eliminating from the
computation) the coincidental correct test cases is also reported.
