AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study
This study provides a broad overview for researchers and practitioners in software engineering, but it is incremental as it synthesizes existing surveys without introducing new methods.
The authors conducted a systematic mapping study to comprehensively analyze the use of AI techniques across the microservices life-cycle, identifying 16 research themes that connect quality attributes, AI domains, and DevOps phases, and mapping future challenges and industry applications.
The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.