  In modern software engineering, Continuous Integration (CI) has become an
indispensable step towards systematically managing the life cycles of software
development. Large companies struggle with keeping the pipeline updated and
operational, in useful time, due to the large amount of changes and addition of
features, that build on top of each other and have several developers, working
on different platforms. Associated with such software changes, there is always
a strong component of Testing. As teams and projects grow, exhaustive testing
quickly becomes inhibitive, becoming adamant to select the most relevant test
cases earlier, without compromising software quality. This paper extends recent
studies on applying Reinforcement Learning to optimize testing strategies. We
test its ability to adapt to new environments, by testing it on novel data
extracted from a financial institution, yielding a Normalized percentage of
Fault Detection (NAPFD) of over $0.6$ using the Network Approximator and Test
Case Failure Reward. Additionally, we studied the impact of using Decision Tree
(DT) Approximator as a model for memory representation, which failed to produce
significant improvements relative to Artificial Neural Networks.
