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Record W3093980010 · doi:10.1145/3382025.3414979

Can microservice-based online-retailers be used as an SPL?

2020· article· en· W3093980010 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsUniversité du Québec à MontréalConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroservicesComputer scienceReuseSoftware versioningFeature (linguistics)SoftwareService (business)Software engineeringSet (abstract data type)Feature modelWorld Wide WebDatabaseOperating systemProgramming languageEngineering

Abstract

fetched live from OpenAlex

Microservices are deployable software artifacts that combine a set of business features and expose them to other microservices. Ideally, the reuse and interchanging of microservices should be easy as they are supposed to be independent of each other, both conceptually and technologically. Selecting a service to fulfill a given feature (e.g., managing a cart in a website) recalls the way Software Product Lines (SPL) allow variability. However, in practice, interchanging microservices requires knowing the features that the services propose, how they communicate with other services and their types. In this work, we propose to analyze service dependencies as feature dependencies, at the feature, structural, technological, and versioning level, to assess the interchangeability of services. We analyze six community-selected use-cases and report that services are non-interchangeable systematically.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.542

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 head0.034
GPT teacher head0.264
Teacher spread0.230 · 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