feat(test): Enable the test fuzzer for Wasm

Cargo.toml

@@ -152,8 +152,12 @@ jsonrpsee = { version = "0.24.7", features = ["client-core"] }
 flate2 = "1.0.24"
 color-eyre = "0.6.2"
 rand = "0.8.5"
-proptest = "1.2.0"
-proptest-derive = "0.4.0"
+# The `fork` and `timeout` feature doesn't compile with Wasm (wait-timeout doesn't find the `imp` module).
+proptest = { version = "1.6.0", default-features = false, features = [
+    "std",
+    "bit-set",
+] }
+proptest-derive = "0.5.0"
 rayon = "1.8.0"
 sha2 = { version = "0.10.6", features = ["compress"] }
 sha3 = "0.10.6"

tooling/fuzzer/src/dictionary/mod.rs

@@ -33,9 +33,11 @@ pub(super) fn build_dictionary_from_program<F: AcirField>(program: &Program<F>)
     constants
 }
 
+/// Collect `Field` values used in the opcodes of an ACIR circuit.
 fn build_dictionary_from_circuit<F: AcirField>(circuit: &Circuit<F>) -> HashSet<F> {
     let mut constants: HashSet<F> = HashSet::new();
 
+    /// Pull out all the fields from an expression.
     fn insert_expr<F: AcirField>(dictionary: &mut HashSet<F>, expr: &Expression<F>) {
         let quad_coefficients = expr.mul_terms.iter().map(|(k, _, _)| *k);
         let linear_coefficients = expr.linear_combinations.iter().map(|(k, _)| *k);
@@ -104,6 +106,7 @@ fn build_dictionary_from_circuit<F: AcirField>(circuit: &Circuit<F>) -> HashSet<
     constants
 }
 
+/// Collect `Field` values used in the opcodes of a Brillig function.
 fn build_dictionary_from_unconstrained_function<F: AcirField>(
     function: &BrilligBytecode<F>,
 ) -> HashSet<F> {

tooling/fuzzer/src/lib.rs

@@ -22,7 +22,7 @@ use types::{CaseOutcome, CounterExampleOutcome, FuzzOutcome, FuzzTestResult};
 
 use noirc_artifacts::program::ProgramArtifact;
 
-/// An executor for Noir programs which which provides fuzzing support using [`proptest`].
+/// An executor for Noir programs which provides fuzzing support using [`proptest`].
 ///
 /// After instantiation, calling `fuzz` will proceed to hammer the program with
 /// inputs, until it finds a counterexample. The provided [`TestRunner`] contains all the
@@ -38,22 +38,22 @@ pub struct FuzzedExecutor<E> {
     runner: TestRunner,
 }
 
-impl<
-        E: Fn(
-            &Program<FieldElement>,
-            WitnessMap<FieldElement>,
-        ) -> Result<WitnessStack<FieldElement>, String>,
-    > FuzzedExecutor<E>
+impl<E> FuzzedExecutor<E>
+where
+    E: Fn(
+        &Program<FieldElement>,
+        WitnessMap<FieldElement>,
+    ) -> Result<WitnessStack<FieldElement>, String>,
 {
-    /// Instantiates a fuzzed executor given a testrunner
+    /// Instantiates a fuzzed executor given a [TestRunner].
     pub fn new(program: ProgramArtifact, executor: E, runner: TestRunner) -> Self {
         Self { program, executor, runner }
     }
 
     /// Fuzzes the provided program.
     pub fn fuzz(&self) -> FuzzTestResult {
         let dictionary = build_dictionary_from_program(&self.program.bytecode);
-        let strategy = strategies::arb_input_map(&self.program.abi, dictionary);
+        let strategy = strategies::arb_input_map(&self.program.abi, &dictionary);
 
         let run_result: Result<(), TestError<InputMap>> =
             self.runner.clone().run(&strategy, |input_map| {

tooling/fuzzer/src/strategies/int.rs

@@ -4,6 +4,8 @@ use proptest::{
 };
 use rand::Rng;
 
+type BinarySearch = proptest::num::i128::BinarySearch;
+
 /// Strategy for signed ints (up to i128).
 /// The strategy combines 2 different strategies, each assigned a specific weight:
 /// 1. Generate purely random value in a range. This will first choose bit size uniformly (up `bits`
@@ -27,6 +29,7 @@ impl IntStrategy {
         Self { bits, edge_weight: 10usize, random_weight: 50usize }
     }
 
+    /// Generate random values near MIN or the MAX value.
     fn generate_edge_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
         let rng = runner.rng();
 
@@ -40,16 +43,16 @@ impl IntStrategy {
             3 => self.type_max() - offset,
             _ => unreachable!(),
         };
-        Ok(proptest::num::i128::BinarySearch::new(start))
+        Ok(BinarySearch::new(start))
     }
 
     fn generate_random_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
         let rng = runner.rng();
-
         let start: i128 = rng.gen_range(self.type_min()..=self.type_max());
-        Ok(proptest::num::i128::BinarySearch::new(start))
+        Ok(BinarySearch::new(start))
     }
 
+    /// Maximum allowed positive number.
     fn type_max(&self) -> i128 {
         if self.bits < 128 {
             (1i128 << (self.bits - 1)) - 1
@@ -58,6 +61,7 @@ impl IntStrategy {
         }
     }
 
+    /// Minimum allowed negative number.
     fn type_min(&self) -> i128 {
         if self.bits < 128 {
             -(1i128 << (self.bits - 1))
@@ -68,7 +72,7 @@ impl IntStrategy {
 }
 
 impl Strategy for IntStrategy {
-    type Tree = proptest::num::i128::BinarySearch;
+    type Tree = BinarySearch;
     type Value = i128;
 
     fn new_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {

tooling/fuzzer/src/strategies/mod.rs

@@ -11,22 +11,28 @@ use uint::UintStrategy;
 mod int;
 mod uint;
 
+/// Create a strategy for generating random values for an [AbiType].
+///
+/// Uses the `dictionary` for unsigned integer types.
 pub(super) fn arb_value_from_abi_type(
     abi_type: &AbiType,
-    dictionary: HashSet<FieldElement>,
+    dictionary: &HashSet<FieldElement>,
 ) -> SBoxedStrategy<InputValue> {
     match abi_type {
         AbiType::Field => vec(any::<u8>(), 32)
             .prop_map(|bytes| InputValue::Field(FieldElement::from_be_bytes_reduce(&bytes)))
             .sboxed(),
         AbiType::Integer { width, sign } if sign == &Sign::Unsigned => {
-            UintStrategy::new(*width as usize, dictionary)
+            // We've restricted the type system to only allow u64s as the maximum integer type.
+            let width = (*width).min(64);
+            UintStrategy::new(width as usize, dictionary)
                 .prop_map(|uint| InputValue::Field(uint.into()))
                 .sboxed()
         }
         AbiType::Integer { width, .. } => {
-            let shift = 2i128.pow(*width);
-            IntStrategy::new(*width as usize)
+            let width = (*width).min(64);
+            let shift = 2i128.pow(width);
+            IntStrategy::new(width as usize)
                 .prop_map(move |mut int| {
                     if int < 0 {
                         int += shift
@@ -38,7 +44,6 @@ pub(super) fn arb_value_from_abi_type(
         AbiType::Boolean => {
             any::<bool>().prop_map(|val| InputValue::Field(FieldElement::from(val))).sboxed()
         }
-
         AbiType::String { length } => {
             // Strings only allow ASCII characters as each character must be able to be represented by a single byte.
             let string_regex = format!("[[:ascii:]]{{{length}}}");
@@ -53,12 +58,11 @@ pub(super) fn arb_value_from_abi_type(
 
             elements.prop_map(InputValue::Vec).sboxed()
         }
-
         AbiType::Struct { fields, .. } => {
             let fields: Vec<SBoxedStrategy<(String, InputValue)>> = fields
                 .iter()
                 .map(|(name, typ)| {
-                    (Just(name.clone()), arb_value_from_abi_type(typ, dictionary.clone())).sboxed()
+                    (Just(name.clone()), arb_value_from_abi_type(typ, dictionary)).sboxed()
                 })
                 .collect();
 
@@ -69,25 +73,25 @@ pub(super) fn arb_value_from_abi_type(
                 })
                 .sboxed()
         }
-
         AbiType::Tuple { fields } => {
             let fields: Vec<_> =
-                fields.iter().map(|typ| arb_value_from_abi_type(typ, dictionary.clone())).collect();
+                fields.iter().map(|typ| arb_value_from_abi_type(typ, dictionary)).collect();
             fields.prop_map(InputValue::Vec).sboxed()
         }
     }
 }
 
+/// Given the [Abi] description of a [ProgramArtifact], generate random [InputValue]s for each circuit parameter.
+///
+/// Use the `dictionary` to draw values from for numeric types.
 pub(super) fn arb_input_map(
     abi: &Abi,
-    dictionary: HashSet<FieldElement>,
+    dictionary: &HashSet<FieldElement>,
 ) -> BoxedStrategy<InputMap> {
     let values: Vec<_> = abi
         .parameters
         .iter()
-        .map(|param| {
-            (Just(param.name.clone()), arb_value_from_abi_type(&param.typ, dictionary.clone()))
-        })
+        .map(|param| (Just(param.name.clone()), arb_value_from_abi_type(&param.typ, dictionary)))
         .collect();
 
     values

tooling/fuzzer/src/strategies/uint.rs

@@ -7,14 +7,16 @@ use proptest::{
 };
 use rand::Rng;
 
+type BinarySearch = proptest::num::u128::BinarySearch;
+
 /// Value tree for unsigned ints (up to u128).
 /// The strategy combines 2 different strategies, each assigned a specific weight:
 /// 1. Generate purely random value in a range. This will first choose bit size uniformly (up `bits`
 ///    param). Then generate a value for this bit size.
 /// 2. Generate a random value around the edges (+/- 3 around 0 and max possible value)
 #[derive(Debug)]
 pub struct UintStrategy {
-    /// Bit size of uint (e.g. 128)
+    /// Bit size of uint (e.g. 64)
     bits: usize,
     /// A set of fixtures to be generated
     fixtures: Vec<FieldElement>,
@@ -31,25 +33,29 @@ impl UintStrategy {
     /// # Arguments
     /// * `bits` - Size of uint in bits
     /// * `fixtures` - Set of `FieldElements` representing values which the fuzzer weight towards testing.
-    pub fn new(bits: usize, fixtures: HashSet<FieldElement>) -> Self {
+    pub fn new(bits: usize, fixtures: &HashSet<FieldElement>) -> Self {
         Self {
             bits,
-            fixtures: fixtures.into_iter().collect(),
+            // We can only consider the fixtures which fit into the bit width.
+            fixtures: fixtures.iter().filter(|f| f.num_bits() <= bits as u32).copied().collect(),
             edge_weight: 10usize,
             fixtures_weight: 40usize,
             random_weight: 50usize,
         }
     }
 
+    /// Generate random numbers starting from near 0 or the maximum of the range.
     fn generate_edge_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
         let rng = runner.rng();
         // Choose if we want values around 0 or max
         let is_min = rng.gen_bool(0.5);
         let offset = rng.gen_range(0..4);
         let start = if is_min { offset } else { self.type_max().saturating_sub(offset) };
-        Ok(proptest::num::u128::BinarySearch::new(start))
+        Ok(BinarySearch::new(start))
     }
 
+    /// Pick a random `FieldElement` from the `fixtures` as a starting point for
+    /// generating random numbers.
     fn generate_fixtures_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
         // generate random cases if there's no fixtures
         if self.fixtures.is_empty() {
@@ -58,21 +64,19 @@ impl UintStrategy {
 
         // Generate value tree from fixture.
         let fixture = &self.fixtures[runner.rng().gen_range(0..self.fixtures.len())];
-        if fixture.num_bits() <= self.bits as u32 {
-            return Ok(proptest::num::u128::BinarySearch::new(fixture.to_u128()));
-        }
 
-        // If fixture is not a valid type, generate random value.
-        self.generate_random_tree(runner)
+        Ok(BinarySearch::new(fixture.to_u128()))
     }
 
+    /// Generate random values between 0 and the MAX with the given bit width.
     fn generate_random_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
         let rng = runner.rng();
         let start = rng.gen_range(0..=self.type_max());
 
-        Ok(proptest::num::u128::BinarySearch::new(start))
+        Ok(BinarySearch::new(start))
     }
 
+    /// Maximum integer that fits in the given bit width.
     fn type_max(&self) -> u128 {
         if self.bits < 128 {
             (1 << self.bits) - 1
@@ -83,8 +87,10 @@ impl UintStrategy {
 }
 
 impl Strategy for UintStrategy {
-    type Tree = proptest::num::u128::BinarySearch;
+    type Tree = BinarySearch;
     type Value = u128;
+
+    /// Pick randomly from the 3 available strategies for generating unsigned integers.
     fn new_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
         let total_weight = self.random_weight + self.fixtures_weight + self.edge_weight;
         let bias = runner.rng().gen_range(0..total_weight);