On theory and management of dependencies between models
Bibliographic record
Abstract
Software developers often need to manage dependencies.Unfortunately, software dependencies manifest themselves in various forms, and discussions about dependencies can be challenging due to the very different definitions and relationships that developers may have in mind.To reduce misunderstandings, it may be helpful to categorize the various forms of dependencies.A dependency is a relationship between two (or more) different things.Let us exclude relationships with more than two participants and concentrate on binary relations to simplify considerations.During a typical development process, dependencies may emerge across all forms of artifacts, including requirement statements, explicit models, source code, and (readily compiled and deployable) system elements.To be precise, we distinguish development artifacts (which include, e.g., UML/SysML models and source code) and the system elements.For example, an object-oriented system consists of implemented classes and their (logical) aggregations in the form of subsystems and components.System elements are to be distinguished from models and source code, which aggregate into packages, directories, branches, or even (version-controlled) projects.Both sides, i.e., the system and the artifacts describing it, are not entirely independent of each other.Java, in particular, has done a tremendous job reliably connecting classes and their source files in an almost one-to-one relation.Colloquially, we thus do not need to distinguish between a class and its describing source file anymore.However, in this article we mention these two sides because the term "dependency" is used within both sides.Projects depend on each other; components depend on each other; and models depend on each 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.
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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.028 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".