Problem
Research questions and friction points this paper is trying to address.
AI in Microservices
Service Quality Improvement
Automation and Productization of Models
AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study
🤖 AI Summary
This study addresses the fragmented and insufficiently systematic application of AI technologies across the microservice lifecycle—particularly within DevOps stages. To this end, it conducts the first panoramic systematic mapping study (SMS), synthesizing 16 core research themes to construct a three-dimensional AI–quality-attribute–engineering-phase association model. Through thematic clustering and cross-dimensional evolutionary analysis, the study uncovers temporal patterns in AI-enabled microservices—including automation in modeling, intelligent operations, and trustworthy deployment—while identifying technological rise-and-fall trends and industrial adoption bottlenecks. Key challenges are clarified: model lightweighting, closed-loop feedback mechanisms, and cross-environment transferability. The work proposes an evolution path for deployable, AI-driven microservice automation prototypes, offering both a theoretical framework and empirical foundation for AI-native microservice engineering practice.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI in Microservices Lifecycle
Model Auto-generation
Automation in Industry Applications
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