  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. We have developed a new
tool called Neural Network Embeeding for Test Case Prioritization (NNE-TCP) is
a novel Machine-Learning (ML) framework that analyses which files were modified
when there was a test status transition and learns relationships between these
files and tests by mapping them into multidimensional vectors and grouping them
by similarity. When new changes are made, tests that are more likely to be
linked to the files modified are prioritized, reducing the resources needed to
find newly introduced faults. Furthermore, NNE-TCP enables entity visualization
in low-dimensional space, allowing for other manners of grouping files and
tests by similarity and to reduce redundancies. By applying NNE-TCP, we show
for the first time that the connection between modified files and tests is
relevant and competitive relative to other traditional methods.
