# TImp: Case Studya Typed Imperative Language

Having covered the basics of QuickChick in the previous chapter, we are ready to dive into a more realistic case study: a typed variant of Imp, the simple imperative language introduced in Logical Foundations.
The version of Imp presented there enforces a syntactic separation between boolean and arithmetic expressions: bexp just ranges over boolean expressions, while aexp ranges over arithmetic ones. Moreover, variables are only allowed in aexp and hence only take numeric values. By contrast, in Typed Imp (TImp) we collapse the expression syntax and allow variables to range over both numbers and booleans. With the unified syntax, we introduce the notion of well-typed Imp expressions and programs (where every variable only ranges over values of a single type throughout the whole program). We then give an operational semantics to TImp in the form of a (partial) evaluation function -- partial since, in the unified syntax, we can write nonsensical expressions such as 0 + True.
A common mantra in functional programming is "well-typed programs cannot go wrong," and TImp is no exception. The soundness property for TImp will state that evaluating well-typed expressions and programs always succeeds.
From the point of view of testing, soundness is interesting because it is a conditional property. As we saw in the previous chapter, testing such properties effectively requires custom generators. In this chapter, we show how to scale the techniques for writing generators explained in QC to more realistic generators for well-typed expressions and programs. In addition, we dicuss the need for custom shrinkers preserving invariants, a problem dual to that of custom generators.
Acknowledgement: We are grateful to Nicolas Koh for important contributions to an early version of this chapter.

# Identifiers, Types and Contexts

## Identifiers

For the type of identifiers of TImp we will use a wrapper around plain natural numbers.
Inductive id :=
| Id : nat id.
We will need one identifier-specific operation, fresh: given any finite set of identifiers, we can produce one that is distinct from all other identifiers in the set.
To compute a fresh id given a list of ids we can just produce the number that is 1 larger than the maximum element:
Fixpoint max_elt (al:list id) : nat :=
match al with
| nil ⇒ 0
| (Id n')::al'max n' (max_elt al')
end.

Definition fresh (al:list id) : id :=
Id (S (max_elt al)).
We will also need a way of testing for equality, which we can derive with the standard dec_eq tactic.
#[export] Instance eq_id_dec (x1 x2 : id) : Dec (x1 = x2).
Proof. dec_eq. Defined.
One advantage of using natural numbers as identifiers is that we can take advantage of the Show instance of nat to print them.
#[export] Instance show_id : Show id :=
{ show x := let '(Id n) := x in show n }.
To generate identifiers for TImp, we will not just generate arbitrary natural numbers. More often than not, we will need to generate a set of identifiers, or pick an identifier from such a set. If we represent a set as a list, we can do the former with a recursive function that generates n fresh nats starting from the empty list. For the latter, we have QuickChick's elems_ combinator.
Fixpoint get_fresh_ids n l :=
match n with
| 0 ⇒ l
| S n'get_fresh_ids n' ((fresh l) :: l)
end.

#### Exercise: 2 stars, standard (genId)

Write a Gen instance for id using the elems_ combinator and get_fresh_ids.
(* FILL IN HERE *)
There remains the question of how to shrink ids. We will answer that question when ids are used later in the chapter. For now, let's leave the Shrink instance as a no-op.
#[export] Instance shrinkId : Shrink id :=
{ shrink x := [] }.

## Types

Here is the type of TImp types:
Inductive ty := TBool | TNat.
That is, TImp has two kinds of values: booleans and natural numbers.
To use ty in testing, we will need Arbitrary, Show, and Dec instances.
In QC.v, we saw how to write such generators by hand. We also saw, however, that this process can largely be automated for simple inductive types (like ty, nat, list, tree, etc.). QuickChick provides a top-level vernacular command to derive such instances.
Derive (Arbitrary, Show) for ty.
(* ==>
GenSizedty is defined
Shrinkty is defined
Showty is defined
*)

Check GenSizedty.
(* ==> GenSizedty : GenSized ty *)
Check Shrinkty.
(* ==> Shrinkty  : Shrink ty   *)
Check Showty.
(* ==> Showty : Show ty     *)
Decidable equality instances are not yet derived fully automatically by QuickChick. However, the boilerplate we have to write is largely straightforward. As we saw in the previous chapters, Dec is a typeclass wrapper around ssreflect's decidable and we can use the dec_eq tactic to automate the process.
#[export] Instance eq_dec_ty (x y : ty) : Dec (x = y).
Proof. dec_eq. Defined.

## List-Based Maps

To encode typing environments (and, later on, states), we will need maps from identifiers to values. However, the function-based representation in the Software Foundations version of Imp is not well suited for testing: we need to be able to access the domain of the map, fold over it, and test for equality; these are all awkward to define for Coq functions. Therefore, we introduce a simple list-based map representation that uses ids as the keys.
The operations we need are:
• empty : To create the empty map.
• get : To look up the binding of an element, if any.
• set : To update the binding of an element.
• dom : To get the list of keys in the map.
The implementation of a map is a simple association list. If a list contains multiple tuples with the same key, then the binding of the key in the map is the one that appears first in the list; that is, later bindings can be shadowed.
Definition Map A := list (id × A).
The empty map is the empty list.
Definition map_empty {A} : Map A := [].
To get the binding of an identifier x, we just need to walk through the list and find the first cons cell where the key is equal to x, if any.
Fixpoint map_get {A} (m : Map A) x : option A :=
match m with
| []None
| (k, v) :: m'if x = k ? then Some v else map_get m' x
end.
To set the binding of an identifier, we just need to cons it at the front of the list.
Definition map_set {A} (m:Map A) (x:id) (v:A) : Map A := (x, v) :: m.
Finally, the domain of a map is just the set of its keys.
Fixpoint map_dom {A} (m:Map A) : list id :=
match m with
| [][]
| (k', v) :: m'k' :: map_dom m'
end.
We next introduce a simple inductive relation, bound_to m x a, that holds precisely when the binding of some identifier x is equal to a in m
Inductive bound_to {A} : Map A id A Prop :=
| Bind : x m a, map_get m x = Some a bound_to m x a.

## Deciding bound_to (optional)

We can now decide whether bound_to m x a holds for a given arrangement of m, x and a. On a first reading, you may prefer to skip the next few paragraphs (until the start of the Context subsection), which deal with partially automating the proofs for such instances.
#[export] Instance dec_bound_to {A : Type} Gamma x (T : A)
`{D : (x y : A), Dec (x = y)}
: Dec (bound_to Gamma x T).
Proof.
constructor. unfold ssrbool.decidable.
destruct (map_get Gamma x) eqn:Get.
After unfolding decidable and destructing map_get Gamma x, we are left with two subgoals. In the first, we know that map_get Gamma x = Some a and effectively want to decide whether map_get Gamma x = Some T or not. Which means we need to decide whether a = T. Thankfully, we can decide that using our hypothesis D.
- destruct (D a T) as [[Eq | NEq]]; subst.
At this point, the first goal can be immediately decided positevely using constructor.
+ left. constructor. auto.
In the second subgoal, we can show that bound_to doesn't hold.
+ right; intro Contra; inversion Contra; subst; clear Contra.
congruence.
Both of these tactic patterns are very common in non-trival Dec instances. It is worth we automating them a bit using LTac.
Abort.
A lot of the time, we can immediately decide a property positivly using constructor applications. That is captured in solve_left.
Ltac solve_left := try solve [left; econstructor; eauto].
Much of the time, we can also immediately decide that a property doesn't hold by assuming it, doing inversion, and using congruence.
Ltac solve_right :=
let Contra := fresh "Contra" in
try solve [right; intro Contra; inversion Contra; subst;
clear Contra; eauto; congruence].
We group both in a single tactic, which does nothing at all if it fails to solve a goal, thanks to try solve.
Ltac solve_sum := solve_left; solve_right.
We can now prove the Dec instance quite concisely.
#[export] Instance dec_bound_to {A : Type} Gamma x (T : A)
`{D : (x y : A), Dec (x = y)}
: Dec (bound_to Gamma x T).
Proof.
constructor. unfold ssrbool.decidable.
destruct (map_get Gamma x) eqn:Get; solve_sum.
destruct (D a T) as [[Eq | NEq]]; subst; solve_sum.
Defined.

## Contexts

Typing contexts in TImp are just maps from identifiers to types.
Definition context := Map ty.
Given a context Gamma and a type T, we can try to generate a random identifier whose binding in Gamma is T.
We use List.filter to extract all of the elements in Gamma whose type is equal to T and then, for each (a,T') that remains, return the name a. We use the oneOf_ combinator to pick an generator from this list at random.
Since the filtered list might be empty we return an option, we use ret None as the default element for oneOf_.
Definition gen_typed_id_from_context (Gamma : context) (T : ty)
: G (option id) :=
oneOf_ (ret None)
(List.map (fun '(x,T')ret (Some x))
(List.filter (fun '(x,T')T = T'?) Gamma)).
We also need to generate typing contexts.
Given some natural number n to serve as the size of the context, we first create its domain, n fresh identifiers. We then create n arbitrary types with vectorOf, using the Gen instance for ty we derived earlier. Finally, we zip (List.combine) the domain with the ranges which creates the (list-based) map.
Definition gen_context (n : nat) : G context :=
let domain := get_fresh_ids n [] in
range <- vectorOf n arbitrary ;;
ret (List.combine domain range).

# Expressions

We are now ready to introduce the syntax of expressions in TImp. The original Imp had two distinct types of expressions, arithmetic and boolean expressions; variables were only allowed to range over natural numbers. In TImp, we extend variables to range over boolean values as well, and we collapse expressions into a single type exp.
Inductive exp : Type :=
| EVar : id exp
| ENum : nat exp
| EPlus : exp exp exp
| EMinus : exp exp exp
| EMult : exp exp exp
| ETrue : exp
| EFalse : exp
| EEq : exp exp exp
| ELe : exp exp exp
| ENot : exp exp
| EAnd : exp exp exp.
To print expressions we derive a Show Instance.
Derive Show for exp.

## Typed Expressions

The following inductive relation characterizes well-typed expressions of a particular type. It is straightforward, using bound_to to access the typing context in the variable case
While the typing relation is almost entirely standard, there is a choice to make about the Ty_Eq rule. The Ty_Eq constructor above requires that the arguments to an equality check are both arithmetic expressions (just like it was in Imp), which simplifies some of the discussion in the remainder of the chapter. We could have allowed for equality checks between booleans as well - that will become an exercise at the end of this chapter.
Once again, we need a decidable instance for the typing relation of TImp. You can skip to the next exercise if you are not interested in specific proof details. Or read later in the chapter about how to automatically derive such instances!
We will need a bit more automation for this proof. We will have a lot of hypotheses of the form:
IH : (T : ty) (Gamma : context),
ssrbool.decidable (Gamma |⊢ e1 \IN T)
Using a brute-force approach, we instantiate such IH with both TNat and TBool, destruct them and then call solve_sum.
The pose proof tactic introduces a new hypothesis in our context, while clear IH removes it so that we don't try the same instantiations again and again.
Ltac solve_inductives Gamma :=
repeat (match goal with
[ IH : _ _, __ ] ⇒
let H1 := fresh "H1" in
pose proof (IH TNat Gamma) as H1;
let H2 := fresh "H2" in
pose proof (IH TBool Gamma) as H2;
clear IH;
destruct H1; destruct H2; solve_sum
end).
Typing in TImp is decidable: given an expression e, a context Gamma and a type T, we can decide whether has_type Gamma e T holds.
#[export] Instance dec_has_type (e : exp) (Gamma : context) (T : ty)
: Dec (Gamma |⊢ e \IN T).
Proof with solve_sum.
constructor.
generalize dependent Gamma.
generalize dependent T.
induction e; intros T Gamma; unfold ssrbool.decidable;
try solve [destruct T; solve_sum];
try solve [destruct T; solve_inductives Gamma].
(* bound_to case: *)
destruct (dec_bound_to Gamma i T); destruct dec; solve_sum.
Defined.

#### Exercise: 3 stars, standard (arbitraryExp)

Derive Arbitrary for expressions. To see how good it is at generating well-typed expressions, write a conditional property cond_prop that is (trivially) always true, with the precondition that some expression is well-typed. Try to check that property like this:
QuickChickWith (updMaxSize stdArgs 3) cond_prop. This idiom sets the maximum-size parameter for all generators to 3, rather the default, which is something larger like 10. When generating examples, QuickChick will start with size 0, gradually increase the size until the maximum size is reached, and then start over. What happens when you vary the size bound?
(* FILL IN HERE *)

## Generating Typed Expressions

Instead of generating expressions and filtering them using has_type, we can be smarter and generate well-typed expressions for a given context directly.
It is common for conditional generators to return options, allowing the possibility of failure if a wrong choice is made internally. For example, if we wanted to generate an expression of type TNat and chose to try to do so by generating a variable, then we might not be able to finish (if the context is empty or only binds booleans).
To chain together two generators with types of the form G (option ...), we need to execute the first generator, match on its result, and, when it is a Some, apply the second generator.
Definition bindGenOpt {A B : Type}
(gma : G (option A)) (k : A G (option B))
: G (option B) :=
ma <- gma ;;
match ma with
| Some ak a
| Noneret None
end.
This pattern is common enough that QuickChick introduces explicit monadic notations, using a double arrow, that have to be explicitly imported.
Import BindOptNotation.
This brings us to our first interesting generator -- one for typed expressions. We assume that Gamma and T are inputs to the generation process. We also use a size parameter to control the depth of generated expressions (i.e., we'll define a sized generator).
Let's start with a much smaller relation: has_type_1 (which consists of just the first constructor of has_type), to demonstrate how to build up complex generators for typed expressions from smaller parts.
Module TypePlayground1.

Inductive has_type : context exp ty Prop :=
| Ty_Var : x T Gamma,
bound_to Gamma x T has_type Gamma (EVar x) T.

End TypePlayground1.
To generate e such that has_type Gamma e T holds, we need to pick one of its constructors (there is only one choice, here) and then try to satisfy its preconditions by generating more things. To satisfy Ty_Var (given Gamma and T), we need to generate x such that bound_to Gamma x T. But we already have such a generator! We just need to wrap it in an EVar.
Definition gen_typed_evar (Gamma : context) (T : ty) : G (option exp) :=
x <-- gen_typed_id_from_context Gamma T;;
ret (Some (EVar x)).
(Note that this is the ret of the GOpt monad.)
Now let's consider an extended typing relation, extending the previous one with all of the constructors of has_type that do not recursively require has_type as a side-condition. These will be the base cases for our final generator.
We can already generate values satisfying Ty_Var using gen_typed_evar. For the rest of the rules, we will need to pattern match on the input T, since Ty_Num can only be used if T = TNat, while Ty_True and Ty_False can only be used if T = TBool.
Definition base' Gamma T : list (G (option exp)) :=
gen_typed_evar Gamma T ::
match T with
| TNat[ n <- arbitrary;; ret (Some (ENum n))]
| TBool[ ret (Some ETrue) ; ret (Some EFalse) ]
end.
We now need to go from a list of (optional) generators to a single generator. We could do that using the oneOf combinator (which chooses uniformly), or the freq combinator (by adding weights).
Instead, we introduce a new one, called backtrack:
backtrack : list (nat × GOpt ?A) → GOpt ?A
Just like freq, backtrack selects one of the generators according to the input weights. Unlike freq, if the chosen generator fails (i.e. produces None), backtrack will discard it, choose another, and keep going until one succeeds or all possibilities are exhausted. Our base-case generator could then be like this:
Definition base Gamma T :=
(2, gen_typed_evar Gamma T) ::
match T with
| TNat[ (2, n <- arbitrary;; ret (Some (ENum n)))]
| TBool[ (1, ret (Some ETrue))
; (1, ret (Some EFalse)) ]
end.

Definition gen_has_type_2 Gamma T := backtrack (base Gamma T).
To see how we handle recursive rules, let's consider a third sub-relation, has_type_3, with just variables and addition:
Typing derivations involving EPlus nodes are binary trees, so we need to add a size parameter to enforce termination. The base case (Ty_Var3) is handled using gen_typed_evar just like before. The non-base case can choose between trying to generate Ty_Var3 and trying to generate Ty_Plus3. For the latter, the input type T must be TNat, otherwise it is not applicable. Once again, this leads to a match on T:
Fixpoint gen_has_type_3 size Gamma T : G (option exp) :=
match size with
| Ogen_typed_evar Gamma T
| S size'
backtrack
([ (1, gen_typed_evar Gamma T) ]
++ match T with
| TNat
[ (size, e1 <-- gen_has_type_3 size' Gamma TNat;;
e2 <-- gen_has_type_3 size' Gamma TNat;;
ret (Some (EPlus e1 e2))) ]
| _[]
end)
end.
Putting all this together, we get the full generator for well-typed expressions.
Fixpoint gen_exp_typed_sized
(size : nat) (Gamma : context) (T : ty)
: G (option exp) :=
let base := base Gamma T in
let recs size' :=
match T with
| TNat
[ (size, e1 <-- gen_exp_typed_sized size' Gamma TNat ;;
e2 <-- gen_exp_typed_sized size' Gamma TNat ;;
ret (Some (EPlus e1 e2)) )
; (size, e1 <-- gen_exp_typed_sized size' Gamma TNat ;;
e2 <-- gen_exp_typed_sized size' Gamma TNat ;;
ret (Some (EMinus e1 e2)) )
; (size, e1 <-- gen_exp_typed_sized size' Gamma TNat ;;
e2 <-- gen_exp_typed_sized size' Gamma TNat ;;
ret (Some (EMult e1 e2)))]
| TBool
[ (size, e1 <-- gen_exp_typed_sized size' Gamma TNat ;;
e2 <-- gen_exp_typed_sized size' Gamma TNat ;;
ret (Some (EEq e1 e2)) )
; (size, e1 <-- gen_exp_typed_sized size' Gamma TNat ;;
e2 <-- gen_exp_typed_sized size' Gamma TNat ;;
ret (Some (ELe e1 e2)) )
; (size, e1 <-- gen_exp_typed_sized size' Gamma TBool ;;
ret (Some (ENot e1)))
; (size, e1 <-- gen_exp_typed_sized size' Gamma TBool ;;
e2 <-- gen_exp_typed_sized size' Gamma TBool ;;
ret (Some (EAnd e1 e2))) ]
end in
match size with
| O
backtrack base
| S size'
backtrack (base ++ recs size')
end.
When writing such complex generators, it's good to have some tests to verify that we are generating what we expect. For example, here we would expect gen_exp_typed_sized to always return expressions that are well typed.
We can use forAll to encode such a property.
Definition gen_typed_has_type :=
let num_vars := 4 in
let top_level_size := 3 in
forAll (gen_context num_vars) (fun Gamma
forAll arbitrary (fun T
forAll (gen_exp_typed_sized top_level_size Gamma T) (fun me
match me with
| Some e(has_type Gamma e T)?
| Nonefalse
end))).

(* QuickChick gen_typed_has_type. *)

# Values and States

## Values

In the original Imp language from Logical Foundations, variables ranged over natural numbers, so states were just maps from identifiers to nat. Since we now want to extend this to also include booleans, we need a type of values that includes both.
Inductive value := VNat : nat value | VBool : bool value.

Derive Show for value.
We can also quickly define a typing relation for values, a Dec instance for it, and a generator for values of a given type.
Inductive has_type_value : value ty Prop :=
| TyVNat : n, has_type_value (VNat n) TNat
| TyVBool : b, has_type_value (VBool b) TBool.

#[export] Instance dec_has_type_value v T : Dec (has_type_value v T).
Proof. constructor; unfold ssrbool.decidable.
destruct v; destruct T; solve_sum.
Defined.

Definition gen_typed_value (T : ty) : G value :=
match T with
| TNatn <- arbitrary;; ret (VNat n)
| TBoolb <- arbitrary;; ret (VBool b)
end.

## States

States in TImp are just maps from identifiers to values
Definition state := Map value.
We introduce an inductive relation that specifies when a state is well typed in a context (that is, when all of its variables are mapped to values of appropriate types).
We encode this in an element-by-element style inductive relation: empty states are only well typed with respect to an empty context, while non-empty states need to map their head identifier to a value of the appropriate type (and their tail must similarly be well typed).
Inductive well_typed_state : context state Prop :=
| TS_Empty : well_typed_state map_empty map_empty
| TS_Elem : x v T st Gamma,
has_type_value v T well_typed_state Gamma st
well_typed_state ((x,T)::Gamma) ((x,v)::st).

#[export] Instance dec_well_typed_state Gamma st : Dec (well_typed_state Gamma st).
Proof.
constructor; unfold ssrbool.decidable.
generalize dependent Gamma.
induction st; intros; destruct Gamma; solve_sum.
destruct a as [a v]; destruct p as [a' T].
destruct (@dec (a = a') _ ); solve_sum.
subst; specialize (IHst Gamma); destruct IHst; solve_sum.
destruct (dec_has_type_value v T); destruct dec; solve_sum.
Defined.

Definition gen_well_typed_state (Gamma : context) : G state :=
sequenceGen (List.map (fun '(x, T)
v <- gen_typed_value T;;
ret (x, v)) Gamma).

# Evaluation

The evaluation function takes a state and an expression and returns an optional value, which can be None if the expression encounters a dynamic type error like trying to perform addition on a boolean.
Fixpoint eval (st : state) (e : exp) : option value :=
match e with
| EVar xmap_get st x
| ENum nSome (VNat n)
| EPlus e1 e2
match eval st e1, eval st e2 with
| Some (VNat n1), Some (VNat n2) ⇒ Some (VNat (n1 + n2))
| _, _None
end
| EMinus e1 e2
match eval st e1, eval st e2 with
| Some (VNat n1), Some (VNat n2) ⇒ Some (VNat (n1 - n2))
| _, _None
end
| EMult e1 e2
match eval st e1, eval st e2 with
| Some (VNat n1), Some (VNat n2) ⇒ Some (VNat (n1 × n2))
| _, _None
end
| ETrueSome (VBool true )
| EFalseSome (VBool false )
| EEq e1 e2
match eval st e1, eval st e2 with
| Some (VNat n1), Some (VNat n2) ⇒ Some (VBool (n1 =? n2))
| _, _None
end
| ELe e1 e2
match eval st e1, eval st e2 with
| Some (VNat n1), Some (VNat n2) ⇒ Some (VBool (n1 <? n2))
| _, _None
end
| ENot e
match eval st e with
| Some (VBool b) ⇒ Some (VBool (negb b))
| _None
end
| EAnd e1 e2
match eval st e1, eval st e2 with
(* Let's include a silly bug here! *)
| Some (VBool b), Some (VNat n2) ⇒ Some (VBool (negb b))
(*  | Some (VBool b1), Some (VBool b2) => Some (VBool (andb b1 b2)) *)
| _, _None
end
end.
We will see in a later chapter (QuickChickTool) how we can use QuickChick to introduce such mutations and have them automatically checked.
Type soundness states that, if we have an expression e of a given type T as well as a well-typed state st, then evaluating e in st will never fail.
Definition isNone {A : Type} (m : option A) :=
match m with
| Nonetrue
| Some _false
end.

Conjecture expression_soundness : Gamma st e T,
well_typed_state Gamma st Gamma |⊢ e \IN T
isNone (eval st e) = false.
To test this property, we construct an appropriate checker:
Definition expression_soundness_exec :=
let num_vars := 4 in
let top_level_size := 3 in
forAll (gen_context num_vars) (fun Gamma
forAll (gen_well_typed_state Gamma) (fun st
forAll arbitrary (fun T
forAll (gen_exp_typed_sized 3 Gamma T) (fun me
match me with
| Some enegb (isNone (eval st e))
| _true
end)))).

(* QuickChick expression_soundness_exec. *)
```     ===>
QuickChecking expression_soundness_exec
[(1,TNat), (2,TNat), (3,TBool), (4,TNat)]
[(1,VNat 0), (2,VNat 0), (3,VBool true), (4,VNat 0)]
TBool
Some EAnd (EAnd (EEq (EVar 4) (EVar 1)) (EEq (ENum 0) (EVar 4))) EFalse
*** Failed after 8 tests and 0 shrinks. (0 discards)
```
Where is the bug?? Looks like we need some shrinking!

## Shrinking for Expressions

Let's see what happens if we use the default shrinker for expressions carelessly.
Derive Shrink for exp.

Definition expression_soundness_exec_firstshrink :=
let num_vars := 4 in
let top_level_size := 3 in
forAll (gen_context num_vars) (fun Gamma
forAll (gen_well_typed_state Gamma) (fun st
forAll arbitrary (fun T
forAllShrink (gen_exp_typed_sized 3 Gamma T) shrink (fun me
match me with
| Some enegb (isNone (eval st e))
| _true
end)))).

(* QuickChick expression_soundness_exec_firstshrink. *)
```     ===>
QuickChecking expression_soundness_exec_firsttry
[(1,TBool), (2,TNat), (3,TBool), (4,TBool)]
[(1,VBool false), (2,VNat 0), (3,VBool true), (4,VBool false)]
TBool
Some EAnd (ENum 0) ETrue
*** Failed after 28 tests and 7 shrinks. (0 discards)
```
The expression shrank to something ill-typed! Since it causes the checker to fail, QuickChick views this as a succesfull shrink, even though this could not actually be produced by our generator and doesn't satisfy our preconditions! One solution would be to check the preconditions in the Checker, filtering out shrinks. But that would be inefficient.
We not only need to shrink expressions, we need to shrink them so that their type is preserved! To accomplish this, we need to intuitively follow the opposite of the procedure we did for generators: look at a typing derivation and see what parts of it we can shrink to while maintaining their types so that the type of the entire thing is preserved.
As in the case of gen_exp_typed, we are going to build up the full shrinker in steps. Let's begin with shrinking constants.
• If e = ENum x for some x, all we can do is try to shrink x.
• If e = ETrue or e = EFalse, we could shrink it to the other. But remember, we don't want to do both, as this would lead to an infinite loop in shrinking! We choose to shrink EFalse to ETrue.
Definition shrink_base (e : exp) : list exp :=
match e with
| ENum nmap ENum (shrink n)
| ETrue[]
| EFalse[ETrue]
| _[]
end.
The next case, EVar, must take the type T to be preserved into account. To shrink an EVar we could try shrinking the inner identifier, but shrinking an identifier by shrinking its natural number representation makes little sense. Better, we can try to shrink the EVar to a constant of the appropriate type.
Definition shrink_evar (T : ty) (e : exp) : list exp :=
match e with
| EVar x
match T with
| TNat[ENum 0]
| TBool[ETrue ; EFalse]
end
| _[]
end.
Finally, we need to be able to shrink the recursive cases. Consider EPlus e1 e2:
• We could try (recursively) shrinking e1 or e2 preserving their TNat type.
• We could try to shrink directly to e1 or e2 since their type is the same as EPlus e1 e2.
On the other hand, consider EEq e1 e2:
• Again, we could recursively shrink e1 or e2.
• But we can't shrink to e1 or e2 since they are of a different type.
• For faster shrinking, we can also try to shrink such expressions to boolean constants directly.
Fixpoint shrink_rec (T : ty) (e : exp) : list exp :=
match e with
| EPlus e1 e2
e1 :: e2
:: (List.map (fun e1'EPlus e1' e2) (shrink_rec T e1))
++ (List.map (fun e2'EPlus e1 e2') (shrink_rec T e2))
| EEq e1 e2
ETrue :: EFalse
:: (List.map (fun e1'EEq e1' e2) (shrink_rec TNat e1))
++ (List.map (fun e2'EEq e1 e2') (shrink_rec TNat e2))
| _[]
end.
Putting it all together yields the following smart shrinker:
Fixpoint shrink_exp_typed (T : ty) (e : exp) : list exp :=
match e with
| EVar _
match T with
| TNat[ENum 0]
| TBool[ETrue ; EFalse]
end
| ENum _[]
| ETrue[]
| EFalse[ETrue]
| EPlus e1 e2
e1 :: e2
:: (List.map (fun e1'EPlus e1' e2) (shrink_exp_typed T e1))
++ (List.map (fun e2'EPlus e1 e2') (shrink_exp_typed T e2))
| EMinus e1 e2
e1 :: e2 :: (EPlus e1 e2)
:: (List.map (fun e1'EMinus e1' e2) (shrink_exp_typed T e1))
++ (List.map (fun e2'EMinus e1 e2') (shrink_exp_typed T e2))
| EMult e1 e2
e1 :: e2 :: (EPlus e1 e2)
:: (List.map (fun e1'EMult e1' e2) (shrink_exp_typed T e1))
++ (List.map (fun e2'EMult e1 e2') (shrink_exp_typed T e2))
| EEq e1 e2
ETrue :: EFalse
:: (List.map (fun e1'EEq e1' e2) (shrink_exp_typed TNat e1))
++ (List.map (fun e2'EEq e1 e2') (shrink_exp_typed TNat e2))
| ELe e1 e2
ETrue :: EFalse :: (EEq e1 e2)
:: (List.map (fun e1'ELe e1' e2) (shrink_exp_typed TNat e1))
++ (List.map (fun e2'ELe e1 e2') (shrink_exp_typed TNat e2))
| ENot e
ETrue :: EFalse :: e :: (List.map ENot (shrink_exp_typed T e))
| EAnd e1 e2
ETrue :: EFalse :: e1 :: e2
:: (List.map (fun e1'EAnd e1' e2) (shrink_exp_typed TBool e1))
++ (List.map (fun e2'EAnd e1 e2') (shrink_exp_typed TBool e2))
end.
As we saw for generators, we can also perform sanity checks on our shrinkers. Here, when the shrinker is applied to an expression of a given type, all of its results should have the same type.
Definition shrink_typed_has_type :=
let num_vars := 4 in
let top_level_size := 3 in
forAll (gen_context num_vars) (fun Gamma
forAll arbitrary (fun T
forAll (gen_exp_typed_sized top_level_size Gamma T) (fun me
match me with
| Some e
List.forallb (fun e'(has_type Gamma e' T)?) (shrink_exp_typed T e)
| _false
end))).

(* QuickChick shrink_typed_has_type. *)

## Back to Soundness

To lift the shrinker to optional expressions, QuickChick provides the following function.
Definition lift_shrink {A}
(shr : A list A) (m : option A)
: list (option A) :=
match m with
| Some xList.map Some (shr x)
| _[]
end.
Armed with shrinking, we can pinpoint the bug in the EAnd branch of the evaluator.
Definition expression_soundness_exec' :=
let num_vars := 4 in
let top_level_size := 3 in
forAll (gen_context num_vars) (fun Gamma
forAll (gen_well_typed_state Gamma) (fun st
forAll arbitrary (fun T
forAllShrink (gen_exp_typed_sized 3 Gamma T)
(lift_shrink (shrink_exp_typed T))
(fun me
match me with
| Some enegb (isNone (eval st e))
| _true
end)))).

(* QuickChick expression_soundness_exec'. *)
```     ===>
QuickChecking expression_soundness_exec'
[(1,TNat), (2,TNat), (3,TNat), (4,TBool)]
[(1,VNat 0), (2,VNat 0), (3,VNat 0), (4,VBool false)]
TBool
Some EAnd ETrue ETrue
*** Failed after 8 tests and 1 shrinks. (0 discards)
```

# Well-Typed Programs

Now we're ready to introduce TImp commands; they are just like the ones in Imp.
Inductive com : Type :=
| CSkip : com
| CAsgn : id exp com
| CSeq : com com com
| CIf : exp com com com
| CWhile : exp com com.

Notation "'SKIP'" :=
CSkip.
Notation "x '::=' a" :=
(CAsgn x a) (at level 60).
Notation "c1 ;;; c2" :=
(CSeq c1 c2) (at level 80, right associativity).
Notation "'WHILE' b 'DO' c 'END'" :=
(CWhile b c) (at level 80, right associativity).
Notation "'TEST' c1 'THEN' c2 'ELSE' c3 'FI'" :=
(CIf c1 c2 c3) (at level 80, right associativity).

Derive Show for com.
(Of course, the derived Show instance is not going to use these notations!)
We can now define what it means for a command to be well typed for a given context. The interesting cases are TAsgn and TIf/TWhile. The first one, ensures that the type of the variable we are assigning to is the same as that of the expression. The latter, requires that the conditional is indeed a boolean expression.

## Decidable instance for well-typed.

A couple of lemmas and a custom tactic will help the decidability proof...
Lemma bind_deterministic Gamma x (T1 T2 : ty) :
bound_to Gamma x T1 bound_to Gamma x T2
T1 = T2.
Proof.
destruct T1; destruct T2; intros H1 H2; eauto;
inversion H1; inversion H2; congruence.
Qed.

Lemma has_type_deterministic Gamma e (T1 T2 : ty) :
has_type e Gamma T1 has_type e Gamma T2
T1 = T2.
Proof.
destruct T1; destruct T2; intros H1 H2; eauto;
inversion H1; inversion H2; subst; eauto; try congruence;
inversion H7; subst;
eapply bind_deterministic; eauto.
Qed.

Ltac solve_det :=
match goal with
| [ H1 : bound_to _ _ ?T1 ,
H2 : bound_to _ _ ?T2_ ] ⇒
assert (T1 = T2) by (eapply bind_deterministic; eauto)
| [ H1 : has_type _ _ ?T1 ,
H2 : has_type _ _ ?T2_ ] ⇒
assert (T1 = T2) by (eapply bind_deterministic; eauto)
end.
Now, here is a brute-force decision procedure for the typing relation (which amounts to a simple typechecker).
#[export] Instance dec_well_typed_com (Gamma : context) (c : com)
: Dec (well_typed_com Gamma c).
Proof with eauto.
constructor. unfold ssrbool.decidable.
induction c; solve_sum.
- destruct (dec_bound_to Gamma i TNat); destruct dec;
destruct (dec_has_type e Gamma TNat); destruct dec;
destruct (dec_bound_to Gamma i TBool); destruct dec;
destruct (dec_has_type e Gamma TBool); destruct dec; solve_sum;
try solve_det; try congruence;
right; intro Contra; inversion Contra; subst; clear Contra;
try solve_det; try congruence;
destruct T; eauto.
- destruct IHc1; destruct IHc2; subst; eauto; solve_sum.
- destruct IHc1; destruct IHc2; subst; eauto; solve_sum.
destruct (dec_has_type e Gamma TBool); destruct dec; solve_sum.
- destruct IHc;
destruct (dec_has_type e Gamma TBool); destruct dec; solve_sum.
Qed.

#### Exercise: 4 stars, standard (arbitrary_well_typed_com)

Write a generator and a shrinker for well_typed programs given some context Gamma. Write some appropriate sanity checks and make sure they give expected results.
(* FILL IN HERE *)
To complete the tour of testing for TImp, here is a (buggy??) evaluation function for commands given a state. To ensure termination, we've included a "fuel" parameter: if it gets to zero we return OutOfGas, signifying that we're not sure if evaluation would have succeeded, failed, or diverged if we'd gone on evaluating.
Inductive result :=
| Success : state result
| Fail : result
| OutOfGas : result.

Fixpoint ceval (fuel : nat) (st : state) (c : com) : result :=
match fuel with
| OOutOfGas
| S fuel'
match c with
| SKIP
Success st
| x ::= e
match eval st e with
| Some vSuccess (map_set st x v)
| _Fail
end
| c1 ;;; c2
match ceval fuel' st c1 with
| Success st'ceval fuel' st' c2
| _Fail
end
| TEST b THEN c1 ELSE c2 FI
match eval st b with
| Some (VBool b) ⇒
ceval fuel' st (if b then c1 else c2)
| _Fail
end
| WHILE b DO c END
match eval st b with
| Some (VBool b') ⇒
if b'
then ceval fuel' st (c ;;; WHILE b DO c END)
else Success st
| _Fail
end
end
end.

Definition isFail r :=
match r with
| Failtrue
| _false
end.
Type soundness: well-typed commands never fail.

#### Exercise: 4 stars, standard (well_typed_state_never_stuck)

Write a checker for the above property, find any bugs, and fix them.
(* FILL IN HERE *)

#### Exercise: 4 stars, standard (ty_eq_polymorphic)

In the has_type relation we allowed equality checks between only arithmetic expressions. Introduce an additional typing rule that allows for equality checks between booleans.
| Ty_Eq : Gamma e1 e2,
Gamma |⊢ e1 \IN TBoolGamma |⊢ e2 \IN TBool
Gamma |⊢ EEq e1 e2 \IN TBool
Make sure you also update the evaluation relation to compare boolean values. Update the generators and shrinkers accordingly to find counterexamples to the buggy properties above.
HINT: When updating the shrinker, you will need to come up with the type of the equated expressions. The Dec instance of has_type will come in handy.

# Automation (Revisited)

QuickChick is under very active development. Our vision is that it should automate most of the tedious parts of testing, while retaining full customizability.
We close this case study with a brief demo of some things it can do now.
Recall the has_type_value property and its corresponding generator:
Inductive has_type_value : valuetyProp :=
| TyVNat : n, has_type_value (VNat n) TNat
| TyVBool : b, has_type_value (VBool b) TBool.

Definition gen_typed_value (T : ty) : G value :=
match T with
| TNatn <- arbitrary;; ret (VNat n)
| TBoolb <- arbitrary;; ret (VBool b)
end.
QuickChick includes a derivation mechanism that can automatically produce such generators -- i.e., generators for data structures satisfying inductively defined properties!
Derive ArbitrarySizedSuchThat for (fun vhas_type_value v T).
===> GenSizedSuchThathas_type_value is defined.
Let's take a closer look at what is being generated (after doing some renaming and reformatting).
```     ===>
GenSizedSuchThathas_type_value = fun T : ty =>
{| arbitrarySizeST :=
let fix aux_arb (size0 : nat) (T : ty) {struct size0}
: G (option value) :=
match size0 with
| 0 => backtrack [(1, match T with
| TBool => ret None
| TNat => n <- arbitrary;;
ret (Some (VNat n))
end)
;(1, match T with
| TBool => b <- arbitrary;;
ret (Some (VBool b)))
| TNat => ret None
end)]
| S _ => backtrack [(1, match T with
| TBool => ret None
| TNat => n <- arbitrary;;
ret (Some (VNat n))
end)
;(1, match T with
| TBool => b <- arbitrary;;
ret (Some (VBool b)))
| TNat => ret None
end)]
end in
fun size0 : nat => aux_arb size0 T |}

: forall T : ty,
GenSizedSuchThat value
(fun v => has_type_value v T)
```
This is a rather more verbose version of the gen_typed_value generator, but the end result is actually exactly the same distribution!
Moreover, QuickChick can also derive DecOpt instances automatically, as well as proofs of correctness!
Derive DecOpt for (has_type_value v T).

Print DecOpthas_type_value.

(* For more information as to how that's possible, look at the PLDI
'22 paper Computing Correctly with Inductive Relations *)

## (More) Typeclasses for Generation

QuickChick provides typeclasses for automating the generation for data satisfying predicates.
Module GenSTPlayground.
A variant that takes a size,...
Class GenSizedSuchThat (A : Type) (P : A Prop) :=
{ arbitrarySizeST : nat G (option A) }.
...an unsized variant,...
Class GenSuchThat (A : Type) (P : A Prop) :=
{ arbitraryST : G (option A) }.
...convenient notation,...
(*
Notation "'genST' x" := (@arbitraryST _ x _) (at level 70).
*)

...and a coercion between the two:
#[export] Instance GenSuchThatOfBounded (A : Type) (P : A Prop)
(H : GenSizedSuchThat A P)
: GenSuchThat A P :=
{ arbitraryST := sized arbitrarySizeST }.

End GenSTPlayground.

## Using "SuchThat" Typeclasses

QuickChick can now (ab)use the typeclass resolution mechanism to perform a bit of black magic:
Conjecture conditional_prop_example :
(x y : nat), x = y x = y.

(* QuickChick conditional_prop_example. *)
```  ==>
QuickChecking conditional_prop_example
+++ Passed 10000 tests (0 discards)
```
Notice the "0 discards": that means that quickchick is using generators that produce x and y such that x = y!

# Acknowledgements

The first version of this material was developed in collaboration with Nicolas Koh.
(* 2023-08-27 16:20 *)