A Denotational Semantic Model for Validating JVML/CLDC Optimizations under Isabelle/HOL
Bibliographic record
Abstract
The main intent of this paper is to present a semantic framework for the validation of JVML/CLDC optimizations. The semantic style of the framework is denotational and rests on an extension of the resource pomsets semantics of Gastin and Mislove [12]. The resource pomsets is a fully abstract semantic model that is based on true concurrency. However, it does not support non-determinism that emerges while interpreting JVML/CLDC programs. In this paper, we present an extension of this model that aims to support unbounded non-determinism. More precisely, we give an overview of the construction of the process space and exhibit its algebraic properties. The elaborated semantics is embedded in the proof assistant Isabelle [28] in order to validate optimizations of JVML/CLDC programs. A case study for the validation of some optimizations of JVML/CLDC programs is also presented. The studied optimizations are: constant propagation and dead assignment elimination.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".