International Workshop on Variability Management for Modern Technologies (VM4ModernTech 2021)
Bibliographic record
Abstract
Variability is an inherent property of software systems that allows developers to deal with the needs of different customers and environments, creating a family of related systems. Variability can be managed in an opportunistic fashion, for example, using clone-and-own, or by employing a systematic approach, for instance, using a software product line (SPL). In the SPL community, variability management has been discussed for systems in various domains, such as defense, avionics, or finance, and for different platforms, such as desktops, web applications, or embedded systems. Unfortunately, other research communities---particularly those working on modern technologies, such as microservice architectures, cyber-physical systems, robotics, cloud computing, autonomous driving, or ML/AI-based systems---are less aware of the state-of-the-art in variability management, which is why they face similar problems and start to redeveloped the same solutions as the SPL community already did. With the International Workshop on Variability Management for Modern Technologies, we aim to foster and strengthen synergies between the communities researching variability management and modern technologies. More precisely, we aim to attract researchers and practitioners to contribute processes, techniques, tools, empirical studies, and problem descriptions or solutions that are related to reuse and variability management for modern technologies. By inviting different communities and establishing collaborations between them, we hope that the workshop can raise the interest of researchers outside the SPL community for variability management, and thus reduce the extent of costly redevelopments in research.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.028 |
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".