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

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.

Authors

Institutions

Publication Details

Journal
Computing
Published
2025-03-29
DOI
https://doi.org/10.1007/s00607-025-01432-z
Citations
9
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
13.38

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Eddy Truyen, Jacopo Soldani, Davide Taibi, Fabiano Pecorelli et al.
9 citations
Computing
Software System Performance and Reliability
13.38
article

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

Eddy Truyen, Jacopo Soldani, Davide Taibi, Fabiano Pecorelli, Daniel Balouek‐Thomert, Ivan Lanese, Justus Bogner, Shahrzad M. Pour, Sergio Moreschini, Xiaozhou Li
article en
9 citations

Abstract

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.

ComputingVol. 107(4)
University of Pisa (IT), Tampere University (FI), Laboratoire des Sciences du Numérique de Nantes (FR), Pegaso University (IT), IMT Atlantique (FR), Vrije Universiteit Amsterdam (NL), University of Bologna (IT), Technical University of Denmark (DK), Nantes Université (FR), University of Oulu (FI), KU Leuven (BE)
KU Leuven, Oulun Yliopisto
Openalex Percentile: Top 1%
Software System Performance and Reliability
13.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.