International Workshop on Variability Management for Modern Technologies (VM4ModernTech 2021)
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
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.
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 | 0.001 |
| 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