Variability management meets microservices
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it