Pragmatic specification of software behavior, configuration, and orchestration: the precision and usability of domain-specific modeling
Bibliographic record
Abstract
In recent discussions, a recurring theme has been the evolving role of specification languages in industrial practice.While formal methods and modeling languages have long aspired to provide unified frameworks for reasoning about software systems, contemporary usage patterns-particularly in largescale industrial settings such as big tech companies-paint a different picture.In several big tech companies, and potentially in other software-intensive businesses, specification exists almost entirely in the service of verification.If a specification does not directly enable the formal verification of an artifact of practical importance-such as code, APIs, configurations, protocols, or policies-it is deemed irrelevant.This perspective frames specification not as an abstraction layer or design blueprint, but as a tightly coupled tool for reasoning about concrete, existing system artifacts.The implications are far-reaching.First, languages are chosen not for their generality or unification power, but rather for their precision and fitness for a specific verification task.They specify specific artifacts, e.g., configuration or orchestration of specific tasks.Task plans are then integrated using sophisticated and consistency-verifying tools.Often, this means designing small domain-specific languages (DSLs) that restrict certain forms of expressiveness, but in B
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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 | 0.028 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".