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//- // Copyright 2017 Jason Lingle // // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or // http://www.apache.org/licenses/LICENSE-2.0> or the MIT license // <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your // option. This file may not be copied, modified, or distributed // except according to those terms. use std::fmt; use std::sync::Arc; use strategy::*; use test_runner::*; /// A strategy for producing arbitrary values of a given type. /// /// `fmt::Debug` is a hard requirement for all strategies currently due to /// `prop_flat_map()`. This constraint will be removed when specialisation /// becomes stable. pub trait Strategy : fmt::Debug { /// The value tree generated by this `Strategy`. /// /// This also implicitly describes the ultimate value type governed by the /// `Strategy`. type Value : ValueTree; /// Generate a new value tree from the given runner. /// /// This may fail if there are constraints on the generated value and the /// generator is unable to produce anything that satisfies them. Any /// failure is wrapped in `TestError::Abort`. fn new_value (&self, runner: &mut TestRunner) -> Result<Self::Value, String>; /// Returns a strategy which produces values transformed by the function /// `fun`. /// /// There is no need (or possibility, for that matter) to define how the /// output is to be shrunken. Shrinking continues to take place in terms of /// the source value. fn prop_map<O : fmt::Debug, F : Fn (<Self::Value as ValueTree>::Value) -> O> (self, fun: F) -> Map<Self, F> where Self : Sized { Map { source: self, fun: Arc::new(fun) } } /// Maps values produced by this strategy into new strategies and picks /// values from those strategies. /// /// `fun` is used to transform the values produced by this strategy into /// other strategies. Values are then chosen from the derived strategies. /// Shrinking proceeds by shrinking individual values as well as shrinking /// the input used to generate the internal strategies. /// /// ## Shrinking /// /// In the case of test failure, shrinking will not only shrink the output /// from the combinator itself, but also the input, i.e., the strategy used /// to generate the output itself. Doing this requires searching the new /// derived strategy for a new failing input. The combinator will generate /// up to `Config::cases` values for this search. /// /// As a result, nested `prop_flat_map`/`Flatten` combinators risk /// exponential run time on this search for new failing values. To ensure /// that test failures occur within a reasonable amount of time, all of /// these combinators share a single "flat map regen" counter, and will /// stop generating new values if it exceeds `Config::max_flat_map_regens`. /// /// ## Example /// /// Generate two integers, where the second is always less than the first, /// without using filtering: /// /// ``` /// #[macro_use] extern crate proptest; /// /// use proptest::prelude::*; /// /// proptest! { /// # /* /// #[test] /// # */ /// fn test_two( /// // Pick integers in the 1..65536 range, and derive a strategy /// // which emits a tuple of that integer and another one which is /// // some value less than it. /// (a, b) in (1..65536).prop_flat_map(|a| (Just(a), 0..a)) /// ) { /// prop_assert!(b < a); /// } /// } /// # /// # fn main() { test_two(); } /// ``` /// /// ## Choosing the right flat-map /// /// `Strategy` has three "flat-map" combinators. They look very similar at /// first, and can be used to produce superficially identical test results. /// For example, the following three expressions all produce inputs which /// are 2-tuples `(a,b)` where the `b` component is less than `a`. /// /// ```no_run /// # #![allow(unused_variables)] /// use proptest::prelude::*; /// /// let flat_map = (1..10).prop_flat_map(|a| (Just(a), 0..a)); /// let ind_flat_map = (1..10).prop_ind_flat_map(|a| (Just(a), 0..a)); /// let ind_flat_map2 = (1..10).prop_ind_flat_map2(|a| 0..a); /// ``` /// /// The three do differ however in terms of how they shrink. /// /// For `flat_map`, both `a` and `b` will shrink, and the invariant that /// `b < a` is maintained. This is a "dependent" or "higher-order" strategy /// in that it remembers that the strategy for choosing `b` is dependent on /// the value chosen for `a`. /// /// For `ind_flat_map`, the invariant `b < a` is maintained, but only /// because `a` does not shrink. This is due to the fact that the /// dependency between the strategies is not tracked; `a` is simply seen as /// a constant. /// /// Finally, for `ind_flat_map2`, the invariant `b < a` is _not_ /// maintained, because `a` can shrink independently of `b`, again because /// the dependency between the two variables is not tracked, but in this /// case the derivation of `a` is still exposed to the shrinking system. /// /// The use-cases for the independent flat-map variants is pretty narrow. /// For the majority of cases where invariants need to be maintained and /// you want all components to shrink, `prop_flat_map` is the way to go. /// `prop_ind_flat_map` makes the most sense when the input to the map /// function is not exposed in the output and shrinking across strategies /// is not expected to be useful. `prop_ind_flat_map2` is useful for using /// related values as starting points while not constraining them to that /// relation. fn prop_flat_map<S : Strategy, F : Fn (<Self::Value as ValueTree>::Value) -> S> (self, fun: F) -> Flatten<Map<Self, F>> where Self : Sized { Flatten::new(Map { source: self, fun: Arc::new(fun) }) } /// Maps values produced by this strategy into new strategies and picks /// values from those strategies while considering the new strategies to be /// independent. /// /// This is very similar to `prop_flat_map()`, but shrinking will *not* /// attempt to shrink the input that produces the derived strategies. This /// is appropriate for when the derived strategies already fully shrink in /// the desired way. /// /// In most cases, you want `prop_flat_map()`. /// /// See `prop_flat_map()` for a more detailed explanation on how the /// three flat-map combinators differ. fn prop_ind_flat_map<S : Strategy, F : Fn (<Self::Value as ValueTree>::Value) -> S> (self, fun: F) -> IndFlatten<Map<Self, F>> where Self : Sized { IndFlatten(Map { source: self, fun: Arc::new(fun) }) } /// Similar to `prop_ind_flat_map()`, but produces 2-tuples with the input /// generated from `self` in slot 0 and the derived strategy in slot 1. /// /// See `prop_flat_map()` for a more detailed explanation on how the /// three flat-map combinators differ differ. fn prop_ind_flat_map2<S : Strategy, F : Fn (<Self::Value as ValueTree>::Value) -> S> (self, fun: F) -> IndFlattenMap<Self, F> where Self : Sized { IndFlattenMap { source: self, fun: Arc::new(fun) } } /// Returns a strategy which only produces values accepted by `fun`. /// /// This results in a very naïve form of rejection sampling and should only /// be used if (a) relatively few values will actually be rejected; (b) it /// isn't easy to express what you want by using another strategy and/or /// `map()`. /// /// There are a lot of downsides to this form of filtering. It slows /// testing down, since values must be generated but then discarded. /// Proptest only allows a limited number of rejects this way (across the /// entire `TestRunner`). Rejection can interfere with shrinking; /// particularly, complex filters may largely or entirely prevent shrinking /// from substantially altering the original value. /// /// Local rejection sampling is still preferable to rejecting the entire /// input to a test (via `TestCaseError::Reject`), however, and the default /// number of local rejections allowed is much higher than the number of /// whole-input rejections. /// /// `whence` is used to record where and why the rejection occurred. fn prop_filter<F : Fn (&<Self::Value as ValueTree>::Value) -> bool> (self, whence: String, fun: F) -> Filter<Self, F> where Self : Sized { Filter { source: self, whence: whence, fun: Arc::new(fun) } } /// Returns a strategy which picks uniformly from `self` and `other`. /// /// When shrinking, if a value from `other` was originally chosen but that /// value can be shrunken no further, it switches to a value from `self` /// and starts shrinking that. /// /// Be aware that chaining `prop_union` calls will result in a very /// right-skewed distribution. If this is not what you want, you can call /// the `.or()` method on the `Union` to add more values to the same union, /// or directly call `Union::new()`. /// /// Both `self` and `other` must be of the same type. To combine /// heterogeneous strategies, call the `boxed()` method on both `self` and /// `other` to erase the type differences before calling `prop_union()`. fn prop_union(self, other: Self) -> Union<Self> where Self : Sized { Union::new(vec![self, other]) } /// Generate a recursive structure with `self` items as leaves. /// /// `recurse` is applied to various strategies that produce the same type /// as `self` with nesting depth _n_ to create a strategy that produces the /// same type with nesting depth _n+1_. Generated structures will have a /// depth between 0 and `depth` and will usually have up to `desired_size` /// total elements, though they may have more. `expected_branch_size` gives /// the expected maximum size for any collection which may contain /// recursive elements and is used to control branch probability to achieve /// the desired size. Passing a too small value can result in trees vastly /// larger than desired. /// /// Note that `depth` only counts branches; i.e., `depth = 0` is a single /// leaf, and `depth = 1` is a leaf or a branch containing only leaves. /// /// In practise, generated values usually have a lower depth than `depth` /// (but `depth` is a hard limit) and almost always under /// `expected_branch_size` (though it is not a hard limit) since the /// underlying code underestimates probabilities. /// /// ## Example /// /// ```rust,norun /// # #![allow(unused_variables)] /// use std::collections::HashMap; /// /// #[macro_use] extern crate proptest; /// use proptest::prelude::*; /// /// /// Define our own JSON AST type /// #[derive(Debug, Clone)] /// enum JsonNode { /// Null, /// Bool(bool), /// Number(f64), /// String(String), /// Array(Vec<JsonNode>), /// Map(HashMap<String, JsonNode>), /// } /// /// # fn main() { /// # /// // Define a strategy for generating leaf nodes of the AST /// let json_leaf = prop_oneof![ /// Just(JsonNode::Null), /// prop::bool::ANY.prop_map(JsonNode::Bool), /// prop::num::f64::ANY.prop_map(JsonNode::Number), /// ".*".prop_map(JsonNode::String), /// ]; /// /// // Now define a strategy for a whole tree /// let json_tree = json_leaf.prop_recursive( /// 4, // No more than 4 branch levels deep /// 64, // Target around 64 total elements /// 16, // Each collection is up to 16 elements long /// |element| prop_oneof![ /// // NB `element` is an `Arc` and we'll need to reference it twice, /// // so we clone it the first time. /// prop::collection::vec(element.clone(), 0..16) /// .prop_map(JsonNode::Array), /// prop::collection::hash_map(".*", element, 0..16) /// .prop_map(JsonNode::Map) /// ].boxed()); /// # } /// ``` fn prop_recursive< F : Fn (Arc<BoxedStrategy<<Self::Value as ValueTree>::Value>>) -> BoxedStrategy<<Self::Value as ValueTree>::Value>> (self, depth: u32, desired_size: u32, expected_branch_size: u32, recurse: F) -> Recursive<BoxedStrategy<<Self::Value as ValueTree>::Value>, F> where Self : Sized + 'static { Recursive { base: Arc::new(self.boxed()), recurse: Arc::new(recurse), depth, desired_size, expected_branch_size, } } /// Erases the type of this `Strategy` so it can be passed around as a /// simple trait object. fn boxed(self) -> BoxedStrategy<<Self::Value as ValueTree>::Value> where Self : Sized + 'static { Box::new(BoxedStrategyWrapper(self)) } /// Wraps this strategy to prevent values from being subject to shrinking. /// /// Suppressing shrinking is useful when testing things like linear /// approximation functions. Ordinarily, proptest will tend to shrink the /// input to the function until the result is just barely outside the /// acceptable range whereas the original input may have produced a result /// far outside of it. Since this makes it harder to see what the actual /// problem is, making the input `NoShrink` allows learning about inputs /// that produce more incorrect results. fn no_shrink(self) -> NoShrink<Self> where Self : Sized { NoShrink(self) } } macro_rules! proxy_strategy { ($typ:ty $(, $lt:tt)*) => { impl<$($lt,)* S : Strategy + ?Sized> Strategy for $typ { type Value = S::Value; fn new_value(&self, runner: &mut TestRunner) -> Result<Self::Value, String> { (**self).new_value(runner) } } }; } proxy_strategy!(Box<S>); proxy_strategy!(&'a S, 'a); proxy_strategy!(&'a mut S, 'a); proxy_strategy!(::std::rc::Rc<S>); proxy_strategy!(::std::sync::Arc<S>); /// A generated value and its associated shrinker. /// /// Conceptually, a `ValueTree` represents a spectrum between a "minimally /// complex" value and a starting, randomly-chosen value. For values such as /// numbers, this can be thought of as a simple binary search, and this is how /// the `ValueTree` state machine is defined. /// /// The `ValueTree` state machine notionally has three fields: low, current, /// and high. Initially, low is the "minimally complex" value for the type, and /// high and current are both the initially chosen value. It can be queried for /// its current state. When shrinking, the controlling code tries simplifying /// the value one step. If the test failure still happens with the simplified /// value, further simplification occurs. Otherwise, the code steps back up /// towards the prior complexity. /// /// The main invariants here are that the "high" value always corresponds to a /// failing test case, and that repeated calls to `complicate()` will return /// `false` only once the "current" value has returned to what it was before /// the last call to `simplify()`. pub trait ValueTree { /// The type of the value produced by this `ValueTree`. type Value : fmt::Debug; /// Returns the current value. fn current(&self) -> Self::Value; /// Attempts to simplify the current value. Notionally, this sets the /// "high" value to the current value, and the current value to a "halfway /// point" between high and low, rounding towards low. /// /// Returns whether any state changed as a result of this call. fn simplify(&mut self) -> bool; /// Attempts to partially undo the last simplification. Notionally, this /// sets the "low" value to one plus the current value, and the current /// value to a "halfway point" between high and the new low, rounding /// towards low. /// /// Returns whether any state changed as a result of this call. fn complicate(&mut self) -> bool; } impl<T : ValueTree + ?Sized> ValueTree for Box<T> { type Value = T::Value; fn current(&self) -> Self::Value { (**self).current() } fn simplify(&mut self) -> bool { (**self).simplify() } fn complicate(&mut self) -> bool { (**self).complicate() } } /// Shorthand for a boxed `Strategy` trait object as produced by /// `Strategy::boxed()`. pub type BoxedStrategy<T> = Box<Strategy<Value = Box<ValueTree<Value = T>>>>; #[derive(Debug)] struct BoxedStrategyWrapper<T>(T); impl<T : Strategy> Strategy for BoxedStrategyWrapper<T> where T::Value : 'static { type Value = Box<ValueTree<Value = <T::Value as ValueTree>::Value>>; fn new_value(&self, runner: &mut TestRunner) -> Result<Self::Value, String> { Ok(Box::new(self.0.new_value(runner)?)) } } /// A `Strategy` which always produces a single value value and never /// simplifies. #[derive(Clone, Copy, Debug)] pub struct Just<T : Clone + fmt::Debug>( /// The value produced by this strategy. pub T); /// Deprecated alias for `Just`. #[deprecated] pub use self::Just as Singleton; impl<T : Clone + fmt::Debug> Strategy for Just<T> { type Value = Self; fn new_value(&self, _: &mut TestRunner) -> Result<Self::Value, String> { Ok(self.clone()) } } impl<T : Clone + fmt::Debug> ValueTree for Just<T> { type Value = T; fn current(&self) -> T { self.0.clone() } fn simplify(&mut self) -> bool { false } fn complicate(&mut self) -> bool { false } } /// Wraps a `Strategy` or `ValueTree` to suppress shrinking of generated /// values. /// /// See `Strategy::no_shrink()` for more details. #[derive(Clone, Copy, Debug)] pub struct NoShrink<T>(T); impl<T : Strategy> Strategy for NoShrink<T> { type Value = NoShrink<T::Value>; fn new_value(&self, runner: &mut TestRunner) -> Result<Self::Value, String> { self.0.new_value(runner).map(NoShrink) } } impl<T : ValueTree> ValueTree for NoShrink<T> { type Value = T::Value; fn current(&self) -> T::Value { self.0.current() } fn simplify(&mut self) -> bool { false } fn complicate(&mut self) -> bool { false } }