  Background: Test flakiness is identified as a major issue that compromises
the regression testing process of complex software systems. Flaky tests
manifest non-deterministic behaviour, send confusing signals to developers, and
break their trust in test suites. Both industrial reports and research studies
highlighted the negative impact of flakiness on software quality and
developers' productivity. While researchers strive to devise solutions that
could help developers addressing test flakiness, the elaboration and assessment
of these solutions are hindered by the lack of datasets large enough to
leverage learning techniques. Aim: To address this lack, we conduct an
exploratory study that investigates a new mean for producing datasets of flaky
tests. Method: We propose an approach that relies on program mutation to inject
flakiness in software tests. In particular, we plan to delete helper statements
from tests to make their outcomes order-dependent, i.e., pass in certain
running orders but fail in other orders. We intend to apply our mutation-based
approach to a set of 14 Java projects to assess the effectiveness of test
mutation in injecting order-dependency and generate a new dataset that could be
used to study flakiness.
