Context: Machine learning (ML) and deep learning (DL) analyze raw data to
extract valuable insights in specific phases. The rise of continuous practices
in software projects emphasizes automating Continuous Integration (CI) with
these learning-based methods, while the growing adoption of such approaches
underscores the need for systematizing knowledge. Objective: Our objective is
to comprehensively review and analyze existing literature concerning
learning-based methods within the CI domain. We endeavour to identify and
analyse various techniques documented in the literature, emphasizing the
fundamental attributes of training phases within learning-based solutions in
the context of CI. Method: We conducted a Systematic Literature Review (SLR)
involving 52 primary studies. Through statistical and thematic analyses, we
explored the correlations between CI tasks and the training phases of
learning-based methodologies across the selected studies, encompassing a
spectrum from data engineering techniques to evaluation metrics. Results: This
paper presents an analysis of the automation of CI tasks utilizing
learning-based methods. We identify and analyze nine types of data sources,
four steps in data preparation, four feature types, nine subsets of data
features, five approaches for hyperparameter selection and tuning, and fifteen
evaluation metrics. Furthermore, we discuss the latest techniques employed,
existing gaps in CI task automation, and the characteristics of the utilized
learning-based techniques. Conclusion: This study provides a comprehensive
overview of learning-based methods in CI, offering valuable insights for
researchers and practitioners developing CI task automation. It also highlights
the need for further research to advance these methods in CI.

该研究综述了持续集成领域中基于学习的方法，探讨了自动化持续集成任务的分析以及相关技术和指标的应用和缺陷，为研究人员和从业者提供了有价值的洞见，并强调了在持续集成中进一步研究这些方法的重要性。