Resource Verification

Not only does Leon allow verification of correctness properties, it also supports establishing bounds on resources such as time or memory usage consumed by programs. The sub-system of Leon that performs verification of resource bounds is called: Orb. It complements correctness verification by allowing users to specify and verify upper bounds on resources consumed by programs.

Why Verify Resource Bounds?

Statically establishing bounds on resources such as time and space consumed by softwares is an important problem that developers are often faced with. While it is desirable to compute resource usage in terms of physical units such as wall-clock time or bytes, this task is usually dependent on the underlying hardware and operating environments. For these reasons, resource usage of programs are often assessed using more abstract, algorithmic metrics that are fairly independent of the runtime infrastructure. These metrics helps establish the asymptotic behavior of the programs, and also can provide more concrete information such as the number of instructions executed by a program in the worst case, or the number of objects allocated in the heap. What Leon provides you is a way to establish bounds on such algorithmic metrics. For instance, you can state and prove that a function sorting a list of integers using insertion sort takes time quadratic in size of the list. After all, most of the development effort is spent on making implementations efficient, and now you can verify the efficiency of your implementations!

The rest of this documentation presents a brief overview of verifying resource usage of programs using Leon. More illustrations are available under the Resourcebounds section of leon web

Proving Abstract Bounds on Resources

Let us start by considering the function count shown below that decrements n until 0. The function takes time O(n). (Subtraction of big integers is considered as a constant time operation.) This can be expressed in Leon as shown below:

import leon.instrumentation._
import leon.invariant._

object ResourceExample {
  def count(n: BigInt): BigInt = {
   require(n >= 0)
    if(n > 0) count(n - 1)
    else n
  } ensuring(res => steps <= ? * n + ?)

Consider the postconditions of the count function. The postcondition uses a reserved keyword steps that refers to the number of steps in the evaluation of the function count on a given input. Generally, it is equal to the number of primitive operations, such as arithmetic-logical operations, performed by the function, and hence is an abstraction of the execution time. The question marks (?) represent unknown coefficients called holes, which needs to be inferred by the system. You will find that Leon is able to automatically infer values for the holes, and complete the bound as steps <= 4 * n + 2. This bound is guaranteed to hold for all executions of count invoked with any n.

Leon also tries to ensure that the inferred bounds are as strong as possible. That is, it tries to ensure that in the inferred bound the coefficient of a term cannot be reduced without increasing the coefficients of the faster growing terms. However, the minimality of the inferred constants is only “best-effort” and not guaranteed.

Importing Inferred Bounds

The leon web interface allows importing the inferred resource bounds and correctness invariants (if any) automatically into the program. To do so click on a tick mark on the right pane, and choose import all invariants. Once all invariants have been imported, the verification phase will get initiated, which may serve to cross check the results.

Finding Counter-examples for Concrete Bounds

For concrete bounds that do not have holes, Leon can discover counter-examples, which are inputs that violate the specification. For instance, Leon will report a counter-example on the following code snippet: (Try it here: leon web)

import leon.instrumentation._
import leon.invariant._

object ResourceExample {
  def count(n: BigInt): BigInt = {
   require(n >= 0)
    if(n > 0) count(n - 1)
    else n
  } ensuring(res => steps <= 2)

The display of the counter-example will consist of an input to the function count and an output. The output would be represented as a pair where the first component refers to the output of the function and the second component to its resource usage. For the above code snippet, Leon would display the following message

The following inputs violate the VC:

n        :=     BigInt(1)

It produced the following output:

(BigInt(0), BigInt(6))

Here, BigInt(6) is the number of steps taken by the function count when the input is BigInt(1). Clearly, it is not less than 2 and hence violates the specification. This feature of Leon can be used to manually test the minimality of the bounds once they have been inferred.

Using Correctness Properties to Establish Bounds

Resource bounds can be stated in combination with other correctness properties. In fact, sometimes the resource bounds themselves may depend on certain correctness properties. For example, consider the function reverse that reverses the elements in a list by calling append. To upper bound the running time of reverse, we need to know that the call append(reverse(tl), Cons(hd, Nil())) in reverse takes time linear in the size of tl (which equals l.tail). To establish this we need two facts, (a) the function append takes time that is linear in the size of its first argument, (b) the size of the list returned by reverse is equal to the size of the input list, which in turn requires that the sizes of the lists returned by append is equal to sum of the sizes of the input lists. These relationships between the sizes of the input and output lists of reverse and append can be stated in their postconditions along with the resource bounds as shown below, and will be used during the verification of bounds.

import leon.instrumentation._
import leon.invariant._
object ListOperations {
  sealed abstract class List
  case class Cons(head: BigInt, tail: List) extends List
  case class Nil() extends List

  def size(l: List): BigInt = (l match {
    case Nil() => 0
    case Cons(_, t) => 1 + size(t)

  def append(l1: List, l2: List): List = (l1 match {
    case Nil() => l2
    case Cons(x, xs) => Cons(x, append(xs, l2))

  }) ensuring (res => size(res) == size(l1) + size(l2) && steps <= ? *size(l1) + ?)

  def reverse(l: List): List = {
    l match {
      case Nil() => l
      case Cons(hd, tl) => append(reverse(tl), Cons(hd, Nil()))
  } ensuring (res => size(res) == size(l) && steps <= ? *(size(l)*size(l)) + ?)

As highlighted by this example there could be deep inter-relationships between the correctness properties and resource bounds. These properties can be seamlessly combined in Leon. Given enough correctness properties Leon can establish resource bounds of complex programs like red-black tree, AVL tree, binomial heaps, and many more. Some of the benchmarks are available in leon web, others can be found in testcases/orb-testcases/ directory.

Resources Supported

Leon currently supports the following resource bounds, which can be used in the postcondition of functions. Let f be a function. The following keywords can be used in its postcondition, and have the following meaning.

  • steps - Number of steps in the evaluation of the function on a given input. This is an abstraction of the time taken by the function on a given input.
  • alloc - Number of objects allocated in the heap by the function on a given input. This is an abstraction of heap memory usage.
  • stack - Stack size in words (4 bytes) consumed by the function on a given input. This is an abstraction of stack memory usage.
  • depth - The longest chain of data dependencies between the operations executed by the function on a given input. This is a measure of parallel execution time.
  • rec - Number of recursive calls, including mutually recursive calls, executed by the function on a given input. This is similar to a loop count of a single loop. Note that calls to functions that do not belong to the same strongly-connected component (SCC) are not counted by this resource.

Dependency on Termination

Proving bounds on resources consumed by a function does not by itself imply termination of the function on all inputs. More importantly, it is possible to prove invalid bounds for non-terminating functions. This holds even for bounds on resources such as steps, which counts the number of evaluation steps. This constraint is because Leon uses induction over the recursive calls made by a function, which is sound only when the function is terminating. Therefore, users are advised to verify the termination of their programs when proving resource or correctness properties. In leon web you can turn on termination from the params memu. To run the Leon termination checker from command line see Command Line Options.

Running from Command Line

The resource verifier can be invoked from command line using --inferInv option. There are several options that can be supplied to configure the behavior and output of the verifier. See Command Line Options for a detailed list of all the options relevant for resource verification. A common use case is shown below:

./leon --inferInv --minbounds=0 --solvers=orb-smt-z3 ./testcases/orb-testcases/timing/AVLTree.scala

The option --inferInv invokes the resource verifier. The option --minbounds=0 instructs the verifier to minimize the bounds using a lower bound of 0 for the coefficients. The option --solvers=orb-smt-z3 configures the verifier to use the SMT Z3 solver through the SMTLIB interface to solve formulas that are generated during inference. This option is recommended if it is necessary to impose hard time limits on resource verification.

Common Pitfalls

  • Using non-inductive bounds

    Like in correctness verification, the bounds that need to established must be provable by inducting over the recursive calls made by the program. For instance, the following function has a bound that is not inductive, and hence cannot be proven.

    import leon.instrumentation._
    import leon.invariant._
    object WrongExample {
      def countUntilN(i: BigInt, n: BigInt): BigInt = {
       require(n >= i && i >= 0)
        if(i < n) countUntilN(i + 1, n)
        else BigInt(0)
      } ensuring(res => steps <= ? * n + ?)

    To prove a linear bound for countUntilN, one should use either steps <= ? * (n - i) + ? or more generally steps <= ? * n + ? * i + ?

Support for Higher-order Functions and Memoization

We have recently extended the tool to verify resource bounds of higher-order functions in the presence of memoization and lazy evaluation. Some examples are available under the heading Memresources in leon web. The technical report Verifying Resource Bounds of Programs with Lazy Evaluation and Memoization provides more details on this extension.


Verification of resource bounds is a significant extension over proving correctness properties. Unfortunately, certain features that are supported in correctness verification are not supported by resource verification as yet. Below are a set of features that are not supported currently.

  • xlang and mutable state
  • Choose operations
  • Class invariants
  • Strings
  • Bit-vectors, bounded integers: Int, Char.


For more examples, check out the directory testcases/orb-testcases/. For any questions, please consult Ravi Madhavan and check the following publications that explain the underlying techniques.


Find below a list people who have contribtued to the resource verification sub-system Orb.

Contributor, Organization, Github Username
  • Ravi Madhavan, EPFL, ravimad
  • Prateek Fegade (during 2015 Summer), IIT Bombay , pratikfegade
  • Sumith Kulal (during 2016 Summer), IIT Bombay, sumith1896

Orb depends extensively on the rest of the code base of Leon. In particular, it relies on functionalities provided by the Leon Frontend, Leon SMT Solver APIs, and Leon Abstract Syntax Trees. These are contributed, and maintained by many others who are not mentioned above.