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

Sergio Moreschini; Shahrzad Pour; Ivan Lanese; Daniel Balouek; Justus Bogner; Xiaozhou Li; Fabiano Pecorelli; Jacopo Soldani; Eddy Truyen; Davide Taibi

doi:10.1007/s00607-025-01432-z

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

Sergio Moreschini, Shahrzad Pour, Ivan Lanese, Daniel Balouek, Justus Bogner, Xiaozhou Li, Fabiano Pecorelli, Jacopo Soldani, Eddy Truyen, Davide Taibi

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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.

Original language	English
Article number	100
Journal	Computing
Volume	107
Issue number	4
Number of pages	50
ISSN	0010-485X
DOIs	https://doi.org/10.1007/s00607-025-01432-z
Publication status	Published - 2025

Keywords

AI
Machine learning
Microservices

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AU - Moreschini, Sergio

AU - Pour, Shahrzad

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AU - Balouek, Daniel

AU - Bogner, Justus

AU - Li, Xiaozhou

AU - Pecorelli, Fabiano

AU - Soldani, Jacopo

AU - Truyen, Eddy

AU - Taibi, Davide

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N2 - 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.

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

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