  Software systems have been continuously evolved and delivered with high
quality due to the widespread adoption of automated tests. A recurring issue
hurting this scenario is the presence of flaky tests, a test case that may pass
or fail non-deterministically. A promising, but yet lacking more empirical
evidence, approach is to collect static data of automated tests and use them to
predict their flakiness. In this paper, we conducted an empirical study to
assess the use of code identifiers to predict test flakiness. To do so, we
first replicate most parts of the previous study of Pinto~et~al.~(MSR~2020).
This replication was extended by using a different ML Python platform
(Scikit-learn) and adding different learning algorithms in the analyses. Then,
we validated the performance of trained models using datasets with other flaky
tests and from different projects. We successfully replicated the results of
Pinto~et~al.~(2020), with minor differences using Scikit-learn; different
algorithms had performance similar to the ones used previously. Concerning the
validation, we noticed that the recall of the trained models was smaller, and
classifiers presented a varying range of decreases. This was observed in both
intra-project and inter-projects test flakiness prediction.
