  Predictive Mutation Testing (PMT) is a technique to predict whether a mutant
will be killed by using machine learning approaches. Researchers have proposed
various machine learning methods for PMT under the cross-project setting.
However, they did not consider the impact of uncovered mutants. A mutant is
uncovered if the statement on which the mutant is generated is not executed by
any test cases. We show that uncovered mutants inflate previous PMT results.
Moreover, we aim at proposing an alternative approach to improve PMT and
suggesting a different interpretation for cross-project PMT. We replicated the
previous PMT research. We also proposed an approach based on the combination of
Random Forest and Gradient Boosting to improve the PMT results. We empirically
evaluated our approach on the same 654 Java projects provided by the previous
PMT literature. Our results indicate that the performance of PMT drastically
decreases in terms of AUC from 0.83 to 0.51. Furthermore, PMT performs worse
than random guesses on 27% of the test projects. The proposed approach improves
the PMT results by achieving the average AUC value of 0.61.
