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Record W3112699636 · doi:10.1145/3424771.3424812

On the Study of Microservices Antipatterns

2020· article· en· W3112699636 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsConcordia UniversityÉcole de Technologie SupérieurePolytechnique MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsMicroservicesCode refactoringComputer scienceSoftware engineeringArchitectureService-oriented architectureProgramming languageSoftwareOperating systemWeb service

Abstract

fetched live from OpenAlex

Microservice architecture has become popular in the last few years as it allows the development of independent, highly reusable, and fine grained services. However, a lack of understanding of its core concepts and the absence of a ground-truth lead to design and implementation decisions, which might be applied often and introduce poorly designed solutions, called antipatterns. The definition of microservice antipatterns is essential for improving the design, maintenance, and evolution of microservice-based systems. Moreover, the few existing specifications and definitions of microservice antipatterns are scattered in the literature. Consequently, we conducted a systematic literature review of 27 papers related to microservices and analyzed 67 open-source microservice-based systems. Based on our analysis, we report in this paper 16 microservice antipatterns. We concisely describe these antipatterns, how they are implemented, and suggest refactoring solutions to remove them.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.245
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it