  Mutation testing is a powerful technique for assessing and improving test
suite quality that artificially introduces bugs and checks whether the test
suites catch them. However, it is also computationally expensive and thus does
not scale to large systems and projects. One promising recent approach to
tackling this scalability problem uses machine learning to predict whether the
tests will detect the synthetic bugs, without actually running those tests.
However, existing predictive mutation testing approaches still misclassify 33%
of detection outcomes on a randomly sampled set of mutant-test suite pairs. We
introduce MutationBERT, an approach for predictive mutation testing that
simultaneously encodes the source method mutation and test method, capturing
key context in the input representation. Thanks to its higher precision,
MutationBERT saves 33% of the time spent by a prior approach on
checking/verifying live mutants. MutationBERT, also outperforms the
state-of-the-art in both same project and cross project settings, with
meaningful improvements in precision, recall, and F1 score. We validate our
input representation, and aggregation approaches for lifting predictions from
the test matrix level to the test suite level, finding similar improvements in
performance. MutationBERT not only enhances the state-of-the-art in predictive
mutation testing, but also presents practical benefits for real-world
applications, both in saving developer time and finding hard to detect mutants.
