Design and Implementation of an Autonomic Code Generator Based on RTPA
Bibliographic record
Abstract
Real-Time Process Algebra (RTPA) is a denotational mathematics for the algebraic modeling and manipulations of software system architectures and behaviors by the Unified Data Models (UDMs) and Unified Process Models (UPMs). On the basis of the RTPA specification and refinement methodologies, automatic software code generation is enabled toward improving software development productivity. This paper examines designing and developing the RTPA-based software code generator (RTPA-CG) that transfers system models in RTPA architectures and behaviors into C++ or Java. A two-phrase strategy has been employed in the design of the code generator. The first phrase analyzes the lexical, syntactical, and type specifications of a software system modeled in RTPA, which results in a set of abstract syntax trees (ASTs). The second phrase translates the ASTs into C++ or Java based on predesigned mapping strategies and code generation rules. The toolkit of RTPA code generator encompasses an RTPA lexer, parser, type-checker, and a code builder. Experimental results show that system models in RTPA can be rigorously processed and corresponding C++/Java code can be automatically generated using the toolkit. The code generated is executable and effective under the support of an RTPA run-time library.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".