Weighing the Evidence: On Relationship Types in Microservice Extraction
Bibliographic record
Abstract
The microservice-based architecture - a SOA-inspired principle of dividing systems into components that communicate with each other using language-agnostic APIs - has gained increased popularity in industry. Yet, migrating a monolithic application to microservices is a challenging task. A number of automated microservice extraction techniques have been proposed to help developers with the migration complexity. These techniques, at large, construct a graph-based representation of an application and cluster its elements into service candidates. The techniques vary by their decomposition goals and, subsequently, types of relationships between application elements that they consider - structural, semantic term similarity, and evolutionary - with each technique utilizing a fixed subset and weighting of these relationship types.In this paper, we perform a multi-method exploratory study with 10 industrial practitioners to investigate (1) the applicability and usefulness of different relationships types during the microservice extraction process and (2) expectations practitioners have for tools utilizing such relationships. Our results show that practitioners often need a "what-if" analysis tool that simultaneously considers multiple relationship types during the extraction process and that there is no fixed way to weight these relationships. Our study also identifies organization- and application-specific considerations that lead practitioners to prefer certain relationship types over others, e.g., the age of the codebase and languages spoken in the organization. It outlines possible strategies to help developers during the extraction process, e.g., the ability to iteratively filter and customize relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.324 | 0.783 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".