Pseudo-random node generation from fuzzer's input using a strict binary encoding #100
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We are going to add a couple of fuzz target generating random Miniscript nodes instead.
There are a few invasive changes that were made following review of the first Bitcoin Core PR: - Renaming NodeType to Fragment - Refactoring various switchs for better readability - Refactoring DecodeScript to use less long lines for parsing of hash fragments
This is the "dumb" version of the Miniscript node generation from fuzzer input. It defines a strict binary encoding and will always generate a node defined from the encoding without "helping" to create valid nodes. It will cut through as soon as it encounters an invalid fragment so hopefully the fuzzer can tend to learn the encoding and generate valid nodes with a higher probability. On a valid generated node a number of invariants are checked, especially around the satisfactions and testing them against the Script interpreter. Co-Authored-By: Pieter Wuille <[email protected]> who made (a previous version of) this non-recursive.
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A first review happened there: darosior/bitcoin@f003bba. This fixes most of the comment but the one regarding |
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ACK 6b37a0c |
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827ed1b Remove leftover types set (Pieter Wuille) b18669a More auto types (Pieter Wuille) 328fabd Remove redundent ComputeScriptLen expression (Pieter Wuille) 492fced Simplify FindInsaneSub (Pieter Wuille) 5fc20e9 Rename s/NodeType/Fragment/ in compiler (Pieter Wuille) a516e1a Improvements for ParseKey / ParseHexStr (Pieter Wuille) Pull request description: Top commit has no ACKs. Tree-SHA512: 41e3a31a2de9df4bb1055c796d6c54a32646b4b7bc54df175a4beaf41298890810fe35d53f645017af11068139d4b6cbf0bee25b67dfbb9d3882f0e4288a1835
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Based on #99 .
This is the "dumb" version of the Miniscript node generation from fuzzer
input. It defines a strict binary encoding and will always generate a
node defined from the encoding without "helping" to create valid nodes.
It will cut through as soon as it encounters an invalid fragment so
hopefully the fuzzer can tend to learn the encoding and generate valid
nodes with a higher probability.
On a valid generated node a number of invariants are checked, especially
around the satisfactions and testing them against the Script
interpreter.