feat: build simple dictionary from inspecting ACIR program

test_programs/noir_test_success/fuzzer_checks/Nargo.toml

@@ -0,0 +1,5 @@
+[package]
+name = "fuzzer_checks"
+type = "bin"
+authors = [""]
+[dependencies]

test_programs/noir_test_success/fuzzer_checks/src/main.nr

@@ -0,0 +1,7 @@
+use dep::std::field::bn254::{TWO_POW_128, assert_gt};
+
+#[test(should_fail_with = "42 is not allowed")]
+fn finds_magic_value(x: u32) {
+    let x = x as u64;
+    assert(2 * x != 42, "42 is not allowed");
+}

tooling/fuzzer/src/dictionary/mod.rs

@@ -0,0 +1,124 @@
+//! This module defines how to build a dictionary of values which are likely to be correspond
+//! to significant inputs during fuzzing by inspecting the [Program] being fuzzed.
+//!
+//! This dictionary can be fed into the fuzzer's [strategy][proptest::strategy::Strategy] in order to bias it towards
+//! generating these values to ensure they get proper coverage.
+use std::collections::HashSet;
+
+use acvm::{
+    acir::{
+        circuit::{
+            brillig::{BrilligBytecode, BrilligInputs},
+            directives::Directive,
+            opcodes::{BlackBoxFuncCall, FunctionInput},
+            Circuit, Opcode, Program,
+        },
+        native_types::Expression,
+    },
+    brillig_vm::brillig::Opcode as BrilligOpcode,
+    AcirField,
+};
+
+/// Constructs a [HashSet<F>] of values pulled from a [Program<F>] which are likely to be correspond
+/// to significant inputs during fuzzing.
+pub(super) fn build_dictionary_from_program<F: AcirField>(program: &Program<F>) -> HashSet<F> {
+    let constrained_dictionaries = program.functions.iter().map(build_dictionary_from_circuit);
+    let unconstrained_dictionaries =
+        program.unconstrained_functions.iter().map(build_dictionary_from_unconstrained_function);
+    let dictionaries = constrained_dictionaries.chain(unconstrained_dictionaries);
+
+    let mut constants: HashSet<F> = HashSet::new();
+    for dictionary in dictionaries {
+        constants.extend(dictionary);
+    }
+    constants
+}
+
+fn build_dictionary_from_circuit<F: AcirField>(circuit: &Circuit<F>) -> HashSet<F> {
+    let mut constants: HashSet<F> = HashSet::new();
+
+    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);
+        let coefficients = linear_coefficients.chain(quad_coefficients);
+
+        dictionary.extend(coefficients.clone());
+        dictionary.insert(expr.q_c);
+
+        // We divide the constant term by any coefficients in the expression to aid solving constraints such as `2 * x - 4 == 0`.
+        let scaled_constants = coefficients.map(|coefficient| expr.q_c / coefficient);
+        dictionary.extend(scaled_constants);
+    }
+
+    fn insert_array_len<F: AcirField, T>(dictionary: &mut HashSet<F>, array: &[T]) {
+        let array_length = array.len() as u128;
+        dictionary.insert(F::from(array_length));
+        dictionary.insert(F::from(array_length - 1));
+    }
+
+    for opcode in &circuit.opcodes {
+        match opcode {
+            Opcode::AssertZero(expr)
+            | Opcode::Call { predicate: Some(expr), .. }
+            | Opcode::MemoryOp { predicate: Some(expr), .. }
+            | Opcode::Directive(Directive::ToLeRadix { a: expr, .. }) => {
+                insert_expr(&mut constants, expr)
+            }
+
+            Opcode::MemoryInit { init, .. } => insert_array_len(&mut constants, init),
+
+            Opcode::BrilligCall { inputs, predicate, .. } => {
+                for input in inputs {
+                    match input {
+                        BrilligInputs::Single(expr) => insert_expr(&mut constants, expr),
+                        BrilligInputs::Array(exprs) => {
+                            exprs.iter().for_each(|expr| insert_expr(&mut constants, expr));
+                            insert_array_len(&mut constants, exprs);
+                        }
+                        BrilligInputs::MemoryArray(_) => (),
+                    }
+                }
+                if let Some(predicate) = predicate {
+                    insert_expr(&mut constants, predicate)
+                }
+            }
+
+            Opcode::BlackBoxFuncCall(BlackBoxFuncCall::RANGE {
+                input: FunctionInput { num_bits, .. },
+            }) => {
+                let field = 1u128.wrapping_shl(*num_bits);
+                constants.insert(F::from(field));
+                constants.insert(F::from(field - 1));
+            }
+            _ => (),
+        }
+    }
+
+    constants
+}
+
+fn build_dictionary_from_unconstrained_function<F: AcirField>(
+    function: &BrilligBytecode<F>,
+) -> HashSet<F> {
+    let mut constants: HashSet<F> = HashSet::new();
+
+    for opcode in &function.bytecode {
+        match opcode {
+            BrilligOpcode::Cast { bit_size, .. } => {
+                let field = 1u128.wrapping_shl(*bit_size);
+                constants.insert(F::from(field));
+                constants.insert(F::from(field - 1));
+            }
+            BrilligOpcode::Const { bit_size, value, .. } => {
+                constants.insert(*value);
+
+                let field = 1u128.wrapping_shl(*bit_size);
+                constants.insert(F::from(field));
+                constants.insert(F::from(field - 1));
+            }
+            _ => (),
+        }
+    }
+
+    constants
+}

tooling/fuzzer/src/lib.rs

@@ -4,9 +4,11 @@
 //! Code is used under the MIT license.
 
 use acvm::{blackbox_solver::StubbedBlackBoxSolver, FieldElement};
+use dictionary::build_dictionary_from_program;
 use noirc_abi::InputMap;
 use proptest::test_runner::{TestCaseError, TestError, TestRunner};
 
+mod dictionary;
 mod strategies;
 mod types;
 
@@ -37,7 +39,8 @@ impl FuzzedExecutor {
 
     /// Fuzzes the provided program.
     pub fn fuzz(&self) -> FuzzTestResult {
-        let strategy = strategies::arb_input_map(&self.program.abi);
+        let dictionary = build_dictionary_from_program(&self.program.bytecode);
+        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/mod.rs

@@ -5,28 +5,22 @@ use proptest::prelude::*;
 use acvm::{AcirField, FieldElement};
 
 use noirc_abi::{input_parser::InputValue, Abi, AbiType, InputMap, Sign};
-use std::collections::BTreeMap;
+use std::collections::{BTreeMap, HashSet};
 use uint::UintStrategy;
 
 mod int;
 mod uint;
 
-proptest::prop_compose! {
-    pub(super) fn arb_field_from_integer(bit_size: u32)(value: u128)-> FieldElement {
-        let width = (bit_size % 128).clamp(1, 127);
-        let max_value = 2u128.pow(width) - 1;
-        let value = value % max_value;
-        FieldElement::from(value)
-    }
-}
-
-pub(super) fn arb_value_from_abi_type(abi_type: &AbiType) -> SBoxedStrategy<InputValue> {
+pub(super) fn arb_value_from_abi_type(
+    abi_type: &AbiType,
+    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)
+            UintStrategy::new(*width as usize, dictionary)
                 .prop_map(|uint| InputValue::Field(uint.into()))
                 .sboxed()
         }
@@ -55,15 +49,17 @@ pub(super) fn arb_value_from_abi_type(abi_type: &AbiType) -> SBoxedStrategy<Inpu
         }
         AbiType::Array { length, typ } => {
             let length = *length as usize;
-            let elements = vec(arb_value_from_abi_type(typ), length..=length);
+            let elements = vec(arb_value_from_abi_type(typ, dictionary), length..=length);
 
             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)).sboxed())
+                .map(|(name, typ)| {
+                    (Just(name.clone()), arb_value_from_abi_type(typ, dictionary.clone())).sboxed()
+                })
                 .collect();
 
             fields
@@ -75,17 +71,23 @@ pub(super) fn arb_value_from_abi_type(abi_type: &AbiType) -> SBoxedStrategy<Inpu
         }
 
         AbiType::Tuple { fields } => {
-            let fields: Vec<_> = fields.iter().map(arb_value_from_abi_type).collect();
+            let fields: Vec<_> =
+                fields.iter().map(|typ| arb_value_from_abi_type(typ, dictionary.clone())).collect();
             fields.prop_map(InputValue::Vec).sboxed()
         }
     }
 }
 
-pub(super) fn arb_input_map(abi: &Abi) -> BoxedStrategy<InputMap> {
+pub(super) fn arb_input_map(
+    abi: &Abi,
+    dictionary: HashSet<FieldElement>,
+) -> BoxedStrategy<InputMap> {
     let values: Vec<_> = abi
         .parameters
         .iter()
-        .map(|param| (Just(param.name.clone()), arb_value_from_abi_type(&param.typ)))
+        .map(|param| {
+            (Just(param.name.clone()), arb_value_from_abi_type(&param.typ, dictionary.clone()))
+        })
         .collect();
 
     values

tooling/fuzzer/src/strategies/uint.rs

@@ -1,3 +1,6 @@
+use std::collections::HashSet;
+
+use acvm::{AcirField, FieldElement};
 use proptest::{
     strategy::{NewTree, Strategy},
     test_runner::TestRunner,
@@ -13,9 +16,12 @@ use rand::Rng;
 pub struct UintStrategy {
     /// Bit size of uint (e.g. 128)
     bits: usize,
-
+    /// A set of fixtures to be generated
+    fixtures: Vec<FieldElement>,
     /// The weight for edge cases (+/- 3 around 0 and max possible value)
     edge_weight: usize,
+    /// The weight for fixtures
+    fixtures_weight: usize,
     /// The weight for purely random values
     random_weight: usize,
 }
@@ -24,8 +30,15 @@ impl UintStrategy {
     /// Create a new strategy.
     /// # Arguments
     /// * `bits` - Size of uint in bits
-    pub fn new(bits: usize) -> Self {
-        Self { bits, edge_weight: 10usize, random_weight: 50usize }
+    /// * `fixtures` - Set of `FieldElements` representing values which the fuzzer weight towards testing.
+    pub fn new(bits: usize, fixtures: HashSet<FieldElement>) -> Self {
+        Self {
+            bits,
+            fixtures: fixtures.into_iter().collect(),
+            edge_weight: 10usize,
+            fixtures_weight: 40usize,
+            random_weight: 50usize,
+        }
     }
 
     fn generate_edge_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
@@ -37,6 +50,22 @@ impl UintStrategy {
         Ok(proptest::num::u128::BinarySearch::new(start))
     }
 
+    fn generate_fixtures_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
+        // generate random cases if there's no fixtures
+        if self.fixtures.is_empty() {
+            return self.generate_random_tree(runner);
+        }
+
+        // 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)
+    }
+
     fn generate_random_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
         let rng = runner.rng();
         let start = rng.gen_range(0..=self.type_max());
@@ -57,11 +86,12 @@ impl Strategy for UintStrategy {
     type Tree = proptest::num::u128::BinarySearch;
     type Value = u128;
     fn new_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
-        let total_weight = self.random_weight + self.edge_weight;
+        let total_weight = self.random_weight + self.fixtures_weight + self.edge_weight;
         let bias = runner.rng().gen_range(0..total_weight);
-        // randomly select one of 2 strategies
+        // randomly select one of 3 strategies
         match bias {
             x if x < self.edge_weight => self.generate_edge_tree(runner),
+            x if x < self.edge_weight + self.fixtures_weight => self.generate_fixtures_tree(runner),
             _ => self.generate_random_tree(runner),
         }
     }