Variability management meets microservices
Bibliographic record
Abstract
A microservice implements a small unit of functionality that it provides through a network using lightweight protocols. So, microservices can be combined to fulfill tasks and implement features of a larger software system---resembling a variability mechanism in the context of a software product line (SPL). Microservices and SPLs have similar goals, namely facilitating reuse and customizing, but they are usually employed in different contexts. Any developer who has access to the network can provide a microservice for any task, while SPLs are usually intended to implement features of a specific domain. Due to their different concepts, using microservices to implement an SPL or adopting SPL practices (e.g., variability management) for microservices is a challenging cross-area research problem. However, both techniques can complement each other, and thus tackling this problem promises benefits for organizations that employ either technique. In this paper, we reason on the importance of advancing in this direction, and sketch six concrete challenges to initiate research, namely (1) feature identification, (2) variability modeling, (3) variable microservice architectures, (4) interchangeability, (5) deep customization, and (6) re-engineering an SPL. We intend these challenges to serve as a starting point for future research in this cross-area research direction---avoiding that the concepts of one area are reinvented in the other.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".