Goal Driven Code Generation for Smart Contract Assemblies
Bibliographic record
Abstract
We are currently witnessing the proliferation of blockchain environments to support a wide spectrum of corporate applications through the use of smart contracts. It is of no surprise that smart contract programming language technology constantly evolves to include not only specialized languages such as Solidity, but also general purpose languages such as GoLang and JavaScript. Furthermore, blockchain technology imposes unique challenges related to the monetary cost of deploying smart contracts, and handling roll-back issues when a smart contract fails. It is therefore evident that the complexity of systems involving smart contracts will only increase over time thus making the maintenance and evolution of such systems a very challenging task. One solution to these problems is to approach the implementation and deployment of such systems in a disciplined and automated way. In this paper, we propose a model-driven approach where the structure and inter-dependencies of smart contract, as well as stakeholder objectives, are denoted by extended goal models which can then be transformed to yield Solidity code that conforms with those models. More specifically, we present first a Domain Specific Language (DSL) to denote extended goal models and second, a transformation process which allows for the Abstract Syntax Trees of such a DSL program to be transformed into Solidity smart contact source code. The transformation process ensures that the generated smart contract skeleton code yields a system that is conformant with the model, which serves as a specification of said system so that subsequent analysis, understanding, and maintenance will be easier to achieve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".