A comprehensive survey on smart contract construction and execution: paradigms, tools, and systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart contracts are regarded as one of the most promising and appealing notions in blockchain technology. Their self-enforcing and event-driven features make some online activities possible without a trusted third party. Nevertheless, problems such as miscellaneous attacks, privacy leakage, and low processing rates prevent them from being widely applied. Various schemes and tools have been proposed to facilitate the construction and execution of secure smart contracts. However, a comprehensive survey for these proposals is absent, hindering new researchers and developers from a quick start. This paper surveys the literature and online resources on smart contract construction and execution over the period 2008-2020. We divide the studies into three categories: (1) design paradigms that give examples and patterns on contract construction, (2) design tools that facilitate the development of secure smart contracts, and (3) extensions and alternatives that improve the privacy or efficiency of the system. We start by grouping the relevant construction schemes into the first two categories. We then review the execution mechanisms in the last category and further divide the state-of-the-art solutions into three classes: private contracts with extra tools, off-chain channels, and extensions on core functionalities. Finally, we summarize several challenges and identify future research directions toward developing secure, privacy-preserving, and efficient smart contracts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it