A framework for software architecture verification
Bibliographic record
Abstract
The authors present a framework for analyzing software architecture descriptions using machine-assisted formal proof. Our approach is based on the translation of an existing architecture description language (ADL) based specification to an alternate mathematical representation. We use higher order logic as mechanized by the Prototype Verification System (PVS) as the formal basis of our framework. Our approach is not tied to any particular ADL. Rather, we define an ADL-independent model of architecture description which formalizes the fundamental design concepts of architecture modeling notations. A key feature of our framework is its flexibility; the architect can choose the design concepts that are modeled. Moreover, since the model is generic to many ADLs, our approach allows for the analysis of systems that are specified using more than one notation. We introduce our model of architecture description, and illustrate the utility of our approach by verifying internal properties of an example architecture, a simple compiler specified in a pipe-and-filter architectural style.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".