A 2017 empirical study of 21 practitioners by Taibi, Lenarduzzi and Pahl established the canonical baseline evidence on why organizations migrate from monolithic systems to microservice architectures — with maintainability and scalability as the top-ranked motivations — and on the migration processes and issues involved. Its concluding paragraph listed four unresolved aspects, one of which remains essentially untouched: capturing how the motivations, benefits, and issues of microservices adoption evolve over time as adopting organizations mature. Later studies produced larger cross-sectional surveys, maturity-level classifications, retrospective single-dimension reports, and anecdotal switch-back cases, but no longitudinal study observing the same organizations, systems, or practitioners at multiple time points. The problem is to supply that longitudinal empirical evidence.

Background

The microservice architectural style decomposes an application into small, independently deployable services — each organized around a business capability and communicating with the others through lightweight, language-agnostic interfaces — in contrast to a monolith, in which all functionality is developed and deployed as a single unit. Because most existing systems were built as monoliths, adopting the style in practice means a migration: a long-running, multi-step restructuring of code, data, teams, and deployment infrastructure. Industry enthusiasm for this migration ran well ahead of empirical evidence, so Taibi, Lenarduzzi and Pahl (2017) conducted what became the foundational empirical study: interviews with 21 practitioners who had migrated to microservices. By comparing three migration processes used by the interviewees, they derived a migration process framework; they ranked the motivations (maintainability and scalability were consistently the most important, with the reduced maintenance effort expected in the long run seen as compensating for a longer return on investment), catalogued common issues encountered during migration, and reported insights into the benefits achieved after adoption. The study's conclusion explicitly flagged four unresolved aspects: (1) generalizability beyond the 21 practitioners; (2) legal issues and security threats, which the study did not cover; (3) the evolution of motivations, benefits, and issues over time, which 'remains to be captured'; and (4) validation of the proposed migration process framework in different organizational contexts and migration scopes. Aspects (1) and (2) have since been substantially addressed by larger and more diverse cross-sectional studies and by dedicated security-focused empirical work, and migration processes have been characterized in other organizational contexts; what no later study has supplied is the genuinely temporal evidence called for by aspect (3). Several adjacent results sharpen why this matters: a maturity-level study of 51 surveyed and 14 interviewed practitioners found that benefits are fulfilled progressively across three maturity stages, suggesting — but not observing — temporal change; a study of 'mature' teams (at least two years of microservices experience) documented post-adoption practices and challenges at a single point in time; and a multivocal literature review — a systematic review that includes grey literature such as blog posts and industry reports alongside peer-reviewed publications — documented four high-profile cases of organizations switching back from microservices to monoliths, motivated by cost, complexity, scalability, performance, and organizational factors — anecdotal evidence that post-adoption experience can diverge sharply from adoption-time motivations. In this literature, 'technical debt' denotes the accumulated future rework cost caused by expedient past design decisions, and 'DevOps' denotes the set of practices combining development and operations into automated, continuous delivery pipelines; both commonly co-occur with microservices adoption and must be disentangled from its effects.

Problem Statement

Provide longitudinal empirical evidence on how, in organizations that have adopted microservice architectures, the motivations that drove adoption, the benefits actually realized, and the issues encountered evolve over time after adoption — the third unresolved aspect stated in the conclusion of Taibi, Lenarduzzi and Pahl (2017). A valid answer must come from a study design that observes the same adopting organizations, systems, or practitioners at multiple points in time, rather than from a single cross-sectional snapshot. The study must (i) report evidence on motivations, benefits, and issues jointly, since the source question concerns their linked evolution as the adoption landscape changes; (ii) distinguish genuine temporal change within the observed units from cross-sectional differences among units at different maturity levels; and (iii) address, at least by explicit design or analysis, the confounding of microservices effects with concurrent changes such as DevOps and cloud-infrastructure adoption. The question is deliberately not restricted to any single organization type, migration scope, or measurement method; both qualitative and quantitative designs are admissible as long as the temporal evidence is systematic.

References

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