MétaCan
Menu
Back to cohort
Record W3082368518 · doi:10.1016/j.patter.2020.100179

A comprehensive survey on smart contract construction and execution: paradigms, tools, and systems

2021· review· en· W3082368518 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePatterns · 2021
Typereview
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of GuelphUniversity of New Brunswick
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsSmart contractComputer scienceBusinessSystems engineeringConstruction engineeringEngineeringComputer securityBlockchain

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.310
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it