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Step 0 should be implementing a way to generate random positive and negative proofs. With good permutations of node types and branch depths. That can be used to generate MPP proofs for unit tests.
Input to the generator should be a given "form" of path. The input should define the order and number of branch and extension nodes. The input should also define whether there is a leaf node. It could correctly end on a branch node. Or it could be incorrectly constructed.
Step 1 should be generating good data sets for unit tests to cover many (edge) cases.
Step 2: add the unit tests.
At a later stage, we could use the generator code to implement something that continuously runs and tests MPP. We may also run fuzzy test suites (echidna) to find errors and/or interesting unit test cases.
Tests should contain positive and negative test cases.
If it is possible to generate correct and incorrect proofs, then that would simplify this a lot.
Step 0 should be implementing a way to generate random positive and negative proofs. With good permutations of node types and branch depths. That can be used to generate MPP proofs for unit tests.
Input to the generator should be a given "form" of path. The input should define the order and number of branch and extension nodes. The input should also define whether there is a leaf node. It could correctly end on a branch node. Or it could be incorrectly constructed.
Step 1 should be generating good data sets for unit tests to cover many (edge) cases.
Step 2: add the unit tests.
At a later stage, we could use the generator code to implement something that continuously runs and tests MPP. We may also run fuzzy test suites (echidna) to find errors and/or interesting unit test cases.
Refer #652
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