Symbols, Trees, and Types

This doc page is specific to features shipped in Scala 2, which have either been removed in Scala 3 or replaced by an alternative. Unless otherwise stated, all the code examples in this page assume you are using Scala 2.



Symbols are used to establish bindings between a name and the entity it refers to, such as a class or a method. Anything you define and can give a name to in Scala has an associated symbol.

Symbols contain all available information about the declaration of an entity (class/object/trait etc.) or a member (vals/vars/defs etc.), and as such are an integral abstraction central to both runtime reflection and compile-time reflection (macros).

A symbol can provide a wealth of information ranging from the basic name method available on all symbols to other, more involved, concepts such as getting the baseClasses from ClassSymbol. Other common use cases of symbols include inspecting members’ signatures, getting type parameters of a class, getting the parameter type of a method or finding out the type of a field.

The Symbol Owner Hierarchy

Symbols are organized in a hierarchy. For example, a symbol that represents a parameter of a method is owned by the corresponding method symbol, a method symbol is owned by its enclosing class, trait, or object, a class is owned by a containing package and so on.

If a symbol does not have an owner, for example, because it refers to a top-level entity, such as a top-level package, then its owner is the special NoSymbol singleton object. Representing a missing symbol, NoSymbol is commonly used in the API to denote an empty or default value. Accessing the owner of NoSymbol throws an exception. See the API docs for the general interface provided by type Symbol


A TypeSymbol represents type, class, and trait declarations, as well as type parameters. Interesting members that do not apply to the more specific ClassSymbols, include isAbstractType, isContravariant, and isCovariant.

  • ClassSymbol: Provides access to all information contained in a class or trait declaration, e.g., name, modifiers (isFinal, isPrivate, isProtected, isAbstractClass, etc.), baseClasses, and typeParams.


The type of term symbols representing val, var, def, and object declarations as well as packages and value parameters.

  • MethodSymbol: The type of method symbols representing def declarations (subclass of TermSymbol). It supports queries like checking whether a method is a (primary) constructor, or whether a method supports variable-length argument lists.
  • ModuleSymbol: The type of module symbols representing object declarations. It allows looking up the class implicitly associated with the object definition via member moduleClass. The opposite look up is also possible. One can go back from a module class to the associated module symbol by inspecting its selfType.termSymbol.

Symbol Conversions

There can be situations where one uses a method that returns an instance of the general Symbol type. In cases like these, it’s possible to convert the more general Symbol type obtained to the specific, more specialized symbol type needed.

Symbol conversions, such as asClass or asMethod, are used to convert to a more specific subtype of Symbol as appropriate (if you want to use the MethodSymbol interface, for example).

For example,

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> class C[T] { def test[U](x: T)(y: U): Int = ??? }
defined class C

scala> val testMember = typeOf[C[Int]].member(TermName("test"))
testMember: scala.reflect.runtime.universe.Symbol = method test

In this case, member returns an instance of Symbol, not MethodSymbol as one might expect. Thus, we must use asMethod to ensure that we obtain a MethodSymbol

scala> testMember.asMethod
res0: scala.reflect.runtime.universe.MethodSymbol = method test

Free symbols

The two symbol types FreeTermSymbol and FreeTypeSymbol have a special status, in the sense that they refer to symbols whose available information is not complete. These symbols are generated in some cases during reification (see the corresponding section about reifying trees for more background). Whenever reification cannot locate a symbol (meaning that the symbol is not available in the corresponding class file, for example, because the symbol refers to a local class), it reifies it as a so-called “free type”, a synthetic dummy symbol that remembers the original name and owner and has a surrogate type signature that closely follows the original. You can check whether a symbol is a free type by calling sym.isFreeType. You can also get a list of all free types referenced by a tree and its children by calling tree.freeTypes. Finally, you can get warnings when reification produces free types by using -Xlog-free-types.


As its name suggests, instances of Type represent information about the type of a corresponding symbol. This includes its members (methods, fields, type aliases, abstract types, nested classes, traits, etc.) either declared directly or inherited, its base types, its erasure and so on. Types also provide operations to test for type conformance or equivalence.

Instantiating Types

In general, there are three ways to instantiate a Type.

  1. via method typeOf on scala.reflect.api.TypeTags, which is mixed into Universe (simplest and most common).
  2. Standard Types, such as Int, Boolean, Any, or Unit are accessible through the available universe.
  3. Manual instantiation using factory methods such as typeRef or polyType on scala.reflect.api.Types, (not recommended).

Instantiating Types With typeOf

To instantiate a type, most of the time, the scala.reflect.api.TypeTags#typeOf method can be used. It takes a type argument and produces a Type instance which represents that argument. For example:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> typeOf[List[Int]]
res0: scala.reflect.runtime.universe.Type = scala.List[Int]

In this example, a scala.reflect.api.Types$TypeRef is returned, which corresponds to the type constructor List, applied to the type argument Int.

Note, however, that this approach requires one to specify by hand the type we’re trying to instantiate. What if we’re interested in obtaining an instance of Type that corresponds to some arbitrary instance? One can simply define a method with a context bound on the type parameter– this generates a specialized TypeTag for us, which we can use to obtain the type of our arbitrary instance:

scala> def getType[T: TypeTag](obj: T) = typeOf[T]
getType: [T](obj: T)(implicit evidence$1: scala.reflect.runtime.universe.TypeTag[T])scala.reflect.runtime.universe.Type

scala> getType(List(1,2,3))
res1: scala.reflect.runtime.universe.Type = List[Int]

scala> class Animal; class Cat extends Animal
defined class Animal
defined class Cat

scala> val a = new Animal
a: Animal = Animal@21c17f5a

scala> getType(a)
res2: scala.reflect.runtime.universe.Type = Animal

scala> val c = new Cat
c: Cat = Cat@2302d72d

scala> getType(c)
res3: scala.reflect.runtime.universe.Type = Cat

Note: Method typeOf does not work for types with type parameters, such as typeOf[List[A]] where A is a type parameter. In this case, one can use scala.reflect.api.TypeTags#weakTypeOf instead. For more details, see the TypeTags section of this guide.

Standard Types

Standard types, such as Int, Boolean, Any, or Unit, are accessible through a universe’s definitions member. For example:

scala> import scala.reflect.runtime.universe
import scala.reflect.runtime.universe

scala> val intTpe = universe.definitions.IntTpe
intTpe: scala.reflect.runtime.universe.Type = Int

The list of standard types is specified in trait StandardTypes in scala.reflect.api.StandardDefinitions.

Common Operations on Types

Types are typically used for type conformance tests or are queried for members. The three main classes of operations performed on types are:

  1. Checking the subtyping relationship between two types.
  2. Checking for equality between two types.
  3. Querying a given type for certain members or inner types.

Subtyping Relationships

Given two Type instances, one can easily test whether one is a subtype of the other using <:< (and in exceptional cases, weak_<:<, explained below)

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> class A; class B extends A
defined class A
defined class B

scala> typeOf[A] <:< typeOf[B]
res0: Boolean = false

scala> typeOf[B] <:< typeOf[A]
res1: Boolean = true

Note that method weak_<:< exists to check for weak conformance between two types. This is typically important when dealing with numeric types.

Scala’s numeric types abide by the following ordering (section 3.5.3 of the Scala language specification):

In some situations Scala uses a more general conformance relation. A type S weakly conforms to a type T, written S <:w T, if S<:T or both S and T are primitive number types and S precedes T in the following ordering:

Weak Conformance Relations
Byte <:w Short
Short <:w Int
Char <:w Int
Int <:w Long
Long <:w Float
Float <:w Double

For example, weak conformance is used to determine the type of the following if-expression:

scala> if (true) 1 else 1d
res2: Double = 1.0

In the if-expression shown above, the result type is defined to be the weak least upper bound of the two types (i.e., the least upper bound with respect to weak conformance).

Thus, since Double is defined to be the least upper bound with respect to weak conformance between Int and Double (according to the spec, shown above), Double is inferred as the type of our example if-expression.

Note that method weak_<:< checks for weak conformance (as opposed to <:< which checks for conformance without taking into consideration weak conformance relations in section 3.5.3 of the spec) and thus returns the correct result when inspecting conformance relations between numeric types Int and Double:

scala> typeOf[Int] weak_<:< typeOf[Double]
res3: Boolean = true

scala> typeOf[Double] weak_<:< typeOf[Int]
res4: Boolean = false

Whereas using <:< would incorrectly report that Int and Double do not conform to each other in any way:

scala> typeOf[Int] <:< typeOf[Double]
res5: Boolean = false

scala> typeOf[Double] <:< typeOf[Int]
res6: Boolean = false

Type Equality

Similar to type conformance, one can easily check the equality of two types. That is, given two arbitrary types, one can use method =:= to see if both denote the exact same compile-time type.

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> def getType[T: TypeTag](obj: T) = typeOf[T]
getType: [T](obj: T)(implicit evidence$1: scala.reflect.runtime.universe.TypeTag[T])scala.reflect.runtime.universe.Type

scala> class A
defined class A

scala> val a1 = new A; val a2 = new A
a1: A = A@cddb2e7
a2: A = A@2f0c624a

scala> getType(a1) =:= getType(a2)
res0: Boolean = true

Note that the precise type info must be the same for both instances. In the following code snippet, for example, we have two instances of List with different type arguments.

scala> getType(List(1,2,3)) =:= getType(List(1.0, 2.0, 3.0))
res1: Boolean = false

scala> getType(List(1,2,3)) =:= getType(List(9,8,7))
res2: Boolean = true

Also important to note is that =:= should always be used to compare types for equality. That is, never use ==, as it can’t check for type equality in the presence of type aliases, whereas =:= can:

scala> type Histogram = List[Int]
defined type alias Histogram

scala> typeOf[Histogram] =:= getType(List(4,5,6))
res3: Boolean = true

scala> typeOf[Histogram] == getType(List(4,5,6))
res4: Boolean = false

As we can see, == incorrectly reports that Histogram and List[Int] have different types.

Querying Types for Members and Declarations

Given a Type, one can also query it for specific members or declarations. A Type’s members include all fields, methods, type aliases, abstract types, nested classes/objects/traits, etc. A Type’s declarations are only those members that were declared (not inherited) in the class/trait/object definition which the given Type represents.

To obtain a Symbol for some specific member or declaration, one need only to use methods members or decls which provide the list of definitions associated with that type. There also exists singular counterparts for each, methods member and decl as well. The signatures of all four are shown below:

/** The member with given name, either directly declared or inherited, an
  * OverloadedSymbol if several exist, NoSymbol if none exist. */
def member(name: Universe.Name): Universe.Symbol

/** The defined or declared members with name name in this type; an
  * OverloadedSymbol if several exist, NoSymbol if none exist. */
def decl(name: Universe.Name): Universe.Symbol

/** A Scope containing all members of this type
  * (directly declared or inherited). */
def members: Universe.MemberScope // MemberScope is a type of
                                  // Traversable, use higher-order
                                  // functions such as map,
                                  // filter, foreach to query!

/** A Scope containing the members declared directly on this type. */
def decls: Universe.MemberScope // MemberScope is a type of
                                       // Traversable, use higher-order
                                       // functions such as map,
                                       // filter, foreach to query!

For example, to look up the map method of List, one can do:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> typeOf[List[_]].member(TermName("map"))
res0: scala.reflect.runtime.universe.Symbol = method map

Note that we pass method member a TermName, since we’re looking up a method. If we were to look up a type member, such as List’s self type, Self, we would pass a TypeName:

scala> typeOf[List[_]].member(TypeName("Self"))
res1: scala.reflect.runtime.universe.Symbol = type Self

We can also query all members or declarations on a type in interesting ways. We can use method members to obtain a Traversable (MemberScopeApi extends Traversable) of Symbols representing all inherited or declared members on a given type, which means that we can use popular higher-order functions on collections like foreach, filter, map, etc., to explore our type’s members. For example, to print the members of List which are private, one must simply do:

scala> typeOf[List[Int]].members.filter(_.isPrivate).foreach(println _)
method super$sameElements
method occCounts
class CombinationsItr
class PermutationsItr
method sequential
method iterateUntilEmpty


Trees are the basis of Scala’s abstract syntax which is used to represent programs. They are also called abstract syntax trees and commonly abbreviated as ASTs.

In Scala reflection, APIs that produce or use trees are the following:

  1. Scala annotations, which use trees to represent their arguments, exposed in Annotation.scalaArgs (for more, see the Annotations section of this guide).
  2. reify, a special method that takes an expression and returns an AST that represents this expression.
  3. Compile-time reflection with macros (outlined in the Macros guide) and runtime compilation with toolboxes both use trees as their program representation medium.

It’s important to note that trees are immutable except for three fields– pos (Position), symbol (Symbol), and tpe (Type), which are assigned when a tree is typechecked.

Kinds of Trees

There are three main categories of trees:

  1. Subclasses of TermTree which represent terms, e.g., method invocations are represented by Apply nodes, object instantiation is achieved using New nodes, etc.
  2. Subclasses of TypTree which represent types that are explicitly specified in program source code, e.g., List[Int] is parsed as AppliedTypeTree. Note: TypTree is not misspelled, nor is it conceptually the same as TypeTreeTypeTree is something different. That is, in situations where Types are constructed by the compiler (e.g., during type inference), they can be wrapped in TypeTree trees and integrated into the AST of the program.
  3. Subclasses of SymTree which introduce or reference definitions. Examples of the introduction of new definitions include ClassDefs which represent class and trait definitions, or ValDef which represent field and parameter definitions. Examples of the reference of existing definitions include Idents which refer to an existing definition in the current scope such as a local variable or a method.

Any other type of tree that one might encounter are typically syntactic or short-lived constructs. For example, CaseDef, which wraps individual match cases; such nodes are neither terms nor types, nor do they carry a symbol.

Inspecting Trees

Scala Reflection provides a handful of ways to visualize trees, all available through a universe. Given a tree, one can:

  • use methods show or toString which print pseudo-Scala code represented by the tree.
  • use method showRaw to see the raw internal tree that the typechecker operates upon.

For example, given the following tree:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> val tree = Apply(Select(Ident(TermName("x")), TermName("$plus")), List(Literal(Constant(2))))
tree: scala.reflect.runtime.universe.Apply = x.$plus(2)

We can use method show (or toString, which is equivalent) to see what that tree represents.

scala> show(tree)
res0: String = x.$plus(2)

As we can see, tree simply adds 2 to term x.

We can also go in the other direction. Given some Scala expression, we can first obtain a tree, and then use method showRaw to see the raw internal tree that the compiler and typechecker operate on. For example, given the expression:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> val expr = reify { class Flower { def name = "Rose" } }
expr: scala.reflect.runtime.universe.Expr[Unit] = ...

Here, reify simply takes the Scala expression it was passed, and returns a Scala Expr, which is simply wraps a Tree and a TypeTag (see the Expr section of this guide for more information about Exprs). We can obtain the tree that expr contains by:

scala> val tree = expr.tree
tree: scala.reflect.runtime.universe.Tree =
  class Flower extends AnyRef {
    def <init>() = {
    def name = "Rose"

And we can inspect the raw tree by simply doing:

scala> showRaw(tree)
res1: String = Block(List(ClassDef(Modifiers(), TypeName("Flower"), List(), Template(List(Ident(TypeName("AnyRef"))), emptyValDef, List(DefDef(Modifiers(), termNames.CONSTRUCTOR, List(), List(List()), TypeTree(), Block(List(Apply(Select(Super(This(typeNames.EMPTY), typeNames.EMPTY), termNames.CONSTRUCTOR), List())), Literal(Constant(())))), DefDef(Modifiers(), TermName("name"), List(), List(), TypeTree(), Literal(Constant("Rose"))))))), Literal(Constant(())))

Traversing Trees

After one understands the structure of a given tree, typically the next step is to extract info from it. This is accomplished by traversing the tree, and it can be done in one of two ways:

  • Traversal via pattern matching.
  • Using a subclass of Traverser

Traversal via Pattern Matching

Traversal via pattern matching is the simplest and most common way to traverse a tree. Typically, one traverses a tree via pattern matching when they are interested in the state of a given tree at a single node. For example, say we simply want to obtain the function and the argument of the only Apply node in the following tree:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> val tree = Apply(Select(Ident(TermName("x")), TermName("$plus")), List(Literal(Constant(2))))
tree: scala.reflect.runtime.universe.Apply = x.$plus(2)

We can simply match on our tree, and in the case that we have an Apply node, just return Apply’s function and argument:

scala> val (fun, arg) = tree match {
     |     case Apply(fn, a :: Nil) => (fn, a)
     | }
fun: scala.reflect.runtime.universe.Tree = x.$plus
arg: scala.reflect.runtime.universe.Tree = 2

We can achieve exactly the same thing a bit more concisely, by putting the pattern match on the left-hand side:

scala> val Apply(fun, arg :: Nil) = tree
fun: scala.reflect.runtime.universe.Tree = x.$plus
arg: scala.reflect.runtime.universe.Tree = 2

Note that Trees can typically be quite complex, with nodes nested arbitrarily deep within other nodes. A simple illustration would be if we were to add a second Apply node to the above tree which serves to add 3 to our sum:

scala> val tree = Apply(Select(Apply(Select(Ident(TermName("x")), TermName("$plus")), List(Literal(Constant(2)))), TermName("$plus")), List(Literal(Constant(3))))
tree: scala.reflect.runtime.universe.Apply = x.$plus(2).$plus(3)

If we apply the same pattern match as above, we obtain the outer Apply node which contains as its function the entire tree representing x.$plus(2) that we saw above:

scala> val Apply(fun, arg :: Nil) = tree
fun: scala.reflect.runtime.universe.Tree = x.$plus(2).$plus
arg: scala.reflect.runtime.universe.Tree = 3

scala> showRaw(fun)
res3: String = Select(Apply(Select(Ident(TermName("x")), TermName("$plus")), List(Literal(Constant(2)))), TermName("$plus"))

In cases where one must do some richer task, such as traversing an entire tree without stopping at a specific node, or collecting and inspecting all nodes of a specific type, using Traverser for traversal might be more advantageous.

Traversal via Traverser

In situations where it’s necessary to traverse an entire tree from top to bottom, using traversal via pattern matching would be infeasible– to do it this way, one must individually handle every type of node that we might come across in the pattern match. Thus, in these situations, typically class Traverser is used.

Traverser makes sure to visit every node in a given tree, in a depth-first search.

To use a Traverser, simply subclass Traverser and override method traverse. In doing so, you can simply provide custom logic to handle only the cases you’re interested in. For example, if, given our x.$plus(2).$plus(3) tree from the previous section, we would like to collect all Apply nodes, we could do:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> val tree = Apply(Select(Apply(Select(Ident(TermName("x")), TermName("$plus")), List(Literal(Constant(2)))), TermName("$plus")), List(Literal(Constant(3))))
tree: scala.reflect.runtime.universe.Apply = x.$plus(2).$plus(3)

scala> object traverser extends Traverser {
     |   var applies = List[Apply]()
     |   override def traverse(tree: Tree): Unit = tree match {
     |     case app @ Apply(fun, args) =>
     |       applies = app :: applies
     |       super.traverse(fun)
     |       super.traverseTrees(args)
     |     case _ => super.traverse(tree)
     |   }
     | }
defined module traverser

In the above, we intend to construct a list of Apply nodes that we find in our given tree.

We achieve this by in effect adding a special case to the already depth-first traverse method defined in superclass Traverser, via subclass traverser’s overridden traverse method. Our special case affects only nodes that match the pattern Apply(fun, args), where fun is some function (represented by a Tree) and args is a list of arguments (represented by a list of Trees).

When a tree matches the pattern (i.e., when we have an Apply node), we simply add it to our List[Apply], applies, and continue our traversal.

Note that, in our match, we call super.traverse on the function fun wrapped in our Apply, and we call super.traverseTrees on our argument list args (essentially the same as super.traverse, but for List[Tree] rather than a single Tree). In both of these calls, our objective is simple– we want to make sure that we use the default traverse method in Traverser because we don’t know whether the Tree that represents fun contains our Apply pattern– that is, we want to traverse the entire sub-tree. Since the Traverser superclass calls this.traverse, passing in every nested sub- tree, eventually our custom traverse method is guaranteed to be called for each sub-tree that matches our Apply pattern.

To trigger the traverse and to see the resulting List of matching Apply nodes, simply do:

scala> traverser.traverse(tree)

scala> traverser.applies
res0: List[scala.reflect.runtime.universe.Apply] = List(x.$plus(2), x.$plus(2).$plus(3))

Creating Trees

When working with runtime reflection, one need not construct trees manually. However, runtime compilation with toolboxes and compile-time reflection with macros both use trees as their program representation medium. In these cases, there are three recommended ways to create trees:

  1. Via method reify (should be preferred wherever possible).
  2. Via method parse on ToolBoxes.
  3. Manual construction (not recommended).

Tree Creation via reify

Method reify simply takes a Scala expression as an argument, and produces that argument’s typed Tree representation as a result.

Tree creation via method reify is the recommended way of creating trees in Scala Reflection. To see why, let’s start with a small example:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> { val tree = reify(println(2)).tree; showRaw(tree) }
res0: String = Apply(Select(Select(This(TypeName("scala")), TermName("Predef")), TermName("println")), List(Literal(Constant(2))))

Here, we simply reify the call to println(2)– that is, we convert the expression println(2) to its corresponding tree representation. Then we output the raw tree. Note that the println method was transformed to scala.Predef.println. Such transformations ensure that regardless of where the result of reify is used, it will not unexpectedly change its meaning. For example, even if this println(2) snippet is later inserted into a block of code that defines its own println, it wouldn’t affect the behavior of the snippet.

This way of creating trees is thus hygenic, in the sense that it preserves bindings of identifiers.

Splicing Trees

Using reify also allows one to compose trees from smaller trees. This is done using Expr.splice.

Note: Expr is reify’s return type. It can be thought of as a simple wrapper which contains a typed Tree, a TypeTag and a handful of reification-relevant methods, such as splice. For more information about Exprs, see the relevant section of this guide.

For example, let’s try to construct a tree representing println(2) using splice:

scala> val x = reify(2)
x: scala.reflect.runtime.universe.Expr[Int(2)] = Expr[Int(2)](2)

scala> reify(println(x.splice))
res1: scala.reflect.runtime.universe.Expr[Unit] = Expr[Unit](scala.this.Predef.println(2))

Here, we reify 2 and println separately, and simply splice one into the other.

Note, however, that there is a requirement for the argument of reify to be valid and typeable Scala code. If instead of the argument to println we wanted to abstract over the println itself, it wouldn’t be possible:

scala> val fn = reify(println)
fn: scala.reflect.runtime.universe.Expr[Unit] = Expr[Unit](scala.this.Predef.println())

scala> reify(fn.splice(2))
<console>:12: error: Unit does not take parameters

As we can see, the compiler assumes that we wanted to reify a call to println with no arguments, when what we really wanted was to capture the name of the function to be called.

These types of use-cases are currently inexpressible when using reify.

Tree Creation via parse on ToolBoxes

Toolboxes can be used to typecheck, compile, and execute abstract syntax trees. A toolbox can also be used to parse a string into an AST.

Note: Using toolboxes requires scala-compiler.jar to be on the classpath.

Let’s see how parse deals with the println example from the previous section:

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> import

scala> val tb = runtimeMirror(getClass.getClassLoader).mkToolBox()
tb:[scala.reflect.runtime.universe.type] =$ToolBoxImpl@7bc979dd

scala> showRaw(tb.parse("println(2)"))
res2: String = Apply(Ident(TermName("println")), List(Literal(Constant(2))))

It’s important to note that, unlike reify, toolboxes aren’t limited by the typeability requirement– although this flexibility is achieved by sacrificing robustness. That is, here we can see that parse, unlike reify, doesn’t reflect the fact that println should be bound to the standard println method.

Note: when using macros, one shouldn’t use ToolBox.parse. This is because there’s already a parse method built into the macro context. For example:

bash$ scala -Yrepl-class-based:false

scala> import scala.language.experimental.macros
import scala.language.experimental.macros

scala> def impl(c: scala.reflect.macros.whitebox.Context) = c.Expr[Unit](c.parse("println(2)"))
def impl(c: scala.reflect.macros.whitebox.Context): c.Expr[Unit]

scala> def test: Unit = macro impl
def test: Unit

scala> test

You can find more about the two Contexts in this Macros article.

Typechecking with ToolBoxes

As earlier alluded to, ToolBoxes enable one to do more than just constructing trees from strings. They can also be used to typecheck, compile, and execute trees.

In addition to outlining the structure of the program, trees also hold important information about the semantics of the program encoded in symbol (a symbol assigned to trees that introduce or reference definitions), and tpe (the type of the tree). By default, these fields are empty, but typechecking fills them in.

When using the runtime reflection framework, typechecking is implemented by ToolBox.typeCheck. When using macros, at compile time one can use the Context.typeCheck method.

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> val tree = reify { "test".length }.tree
tree: scala.reflect.runtime.universe.Tree = "test".length()

scala> import

scala> val tb = runtimeMirror(getClass.getClassLoader).mkToolBox()
tb:[scala.reflect.runtime.universe.type] = ...

scala> val ttree = tb.typeCheck(tree)
ttree: tb.u.Tree = "test".length()

scala> ttree.tpe
res5: tb.u.Type = Int

scala> ttree.symbol
res6: tb.u.Symbol = method length

Here, we simply create a tree that represents a call to "test".length, and use ToolBox tb’s typeCheck method to typecheck the tree. As we can see, ttree gets the correct type, Int, and its Symbol is correctly set.

Tree Creation via Manual Construction

If all else fails, one can manually construct trees. This is the most low-level way to create trees, and it should only be attempted if no other approach works. It sometimes offers greater flexibility when compared with parse, though this flexibility is achieved at a cost of excessive verbosity and fragility.

Our earlier example involving println(2) can be manually constructed as follows:

scala> Apply(Ident(TermName("println")), List(Literal(Constant(2))))
res0: scala.reflect.runtime.universe.Apply = println(2)

The canonical use case for this technique is when the target tree needs to be assembled from dynamically created parts, which don’t make sense in isolation from one another. In that case, reify will most likely be inapplicable, because it requires its argument to be typeable. parse might not work either, since quite often, trees are assembled on sub-expression level, with individual parts being inexpressible as Scala sources.

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