  The software industry is experiencing a surge in the adoption of Continuous
Integration (CI) practices, both in commercial and open-source environments. CI
practices facilitate the seamless integration of code changes by employing
automated building and testing processes. Some frameworks, such as Travis CI
and GitHub Actions have significantly contributed to simplifying and enhancing
the CI process, rendering it more accessible and efficient for development
teams. Despite the availability these CI tools , developers continue to
encounter difficulties in accurately flagging commits as either suitable for CI
execution or as candidates for skipping especially for large projects with many
dependencies. Inaccurate flagging of commits can lead to resource-intensive
test and build processes, as even minor commits may inadvertently trigger the
Continuous Integration process. The problem of detecting CI-skip commits, can
be modeled as binary classification task where we decide to either build a
commit or to skip it. This study proposes a novel solution that leverages Deep
Reinforcement Learning techniques to construct an optimal Decision Tree
classifier that addresses the imbalanced nature of the data. We evaluate our
solution by running a within and a cross project validation benchmark on
diverse range of Open-Source projects hosted on GitHub which showcased superior
results when compared with existing state-of-the-art methods.
