AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study

TL;DR

This systematic mapping study comprehensively explores how AI techniques are applied across the entire microservices life-cycle, revealing research themes, trends, and future challenges in improving quality attributes during DevOps phases.

Contribution

It provides an exhaustive mapping of AI applications in microservices, identifying 16 research themes and connecting AI techniques with quality attributes and DevOps phases.

Findings

16 research themes identified connecting AI, QAs, and DevOps phases

Many studies aim to develop prototypes for automation and industry deployment

Mapping future research challenges and industry domains

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.