Fig 1.
Smart contract vs. traditional contract.
Fig 2.
Smart contracts example.
Table 1.
Centrality and dispersion statistics computed for all the Smart Contract software metrics.
Table 2.
Statements statistics computed for all the Smart Contracts.
Fig 3.
Histogram distributions of the metrics Total lines, Blanks, Function and Payable.
Fig 4.
Histogram distributions of the metrics Events, Mapping, Modifier and Contract.
Fig 5.
Histogram distributions of the metrics Address, Cyclomatic, Comments, ABI, Bytecode and LOCS.
Fig 6.
The average number of interfaces and libraries in Smart Contract.
Fig 7.
The average number of LOC and Bytecodes per Smart Contract.
Fig 8.
Smart Contracts’ LOC distribution vs. pragma version.
Fig 9.
Power law and Log normal best fitting of the metrics Total lines, Blanks, Function and Payable.
Fig 10.
Power law and Log normal best fitting of the metrics Events, Mapping, Modifier and Contract.
Fig 11.
Power law and Log normal best fitting of the metrics Address, Cyclomatic, Comments, ABI, Bytecode and LOCS.
Table 3.
Fitting parameters for the power law and log-normal distributions.
The xmin and α estimated parameters are reported for the Power Law. For the Log-Normal the xmin, log(μ) and log(σ) estimated parameters are reported.