Systematic mapping study examines AI application in CI/CD pipeline optimization, highlighting increasing research trends.
Continuous Integration/Deployment (CI/CD) pipelines are essential in the software engineering field, and improving automation and efficiency in this field requires optimizing them. AI has become a crucial tool for optimizing these pipelines at different stages. This research examines the application of AI approaches to CI/CD pipeline optimization using a systematic mapping study (SMS) to give a global overview of this field. We examined 92 papers published between 2015 and 2025 based on five main criteria: publication year and channel, research type, CI/CD pipeline stage, empirical method, and AI techniques used. The results show a notable increase in research efforts since 2018; conferences and journals are the most popular publication channels, and most of them are the solutions proposal type, which uses hypothesis-based experimentation as an empirical method for evaluation. Moreover, the testing stage of CI/CD is the most targeted using reinforcement learning algorithms.
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Farihane et al. (2025) studied this question.
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