System for verifying the correctness of generated Copilot programs.

copilot-verifier is an add-on to the Copilot Stream DSL for verifying the correctness of C code generated by the copilot-c99 package.

copilot-verifier uses the Crucible symbolic simulator to interpret the semantics of the generated C program and and to produce verification conditions sufficient to guarantee that the meaning of the generated program corresponds in a precise way to the meaning of the original stream specification. The generated verification conditions are then dispatched to SMT solvers to be automatically solved.

Copilot Verifier

Copilot Verifier is an add-on to the Copilot Stream DSL for verifying the correctness of C code generated by the copilot-c99 package.

The main idea of the verifier is to use the Crucible symbolic simulator to interpret the semantics of the generated C program and and to produce verification conditions sufficient to guarantee that the meaning of the generated program corresponds in a precise way to the meaning of the original stream specification. The generated verification conditions are then dispatched to SMT solvers to be automatically solved. We will have more to say about exactly what is meant by this correspondence below.

Copilot Verifier is described in the ICFP 2023 paper Trustworthy Runtime Verification via Bisimulation (Experience Report).

Building

To build the verifier from source, first make sure you have met the following prerequisites:

• Ensure that you have the cabal and ghc executables in your PATH. If you don't already have them, we recommend using ghcup to install them: https://www.haskell.org/ghcup/. We recommend Cabal 3.10 or newer, and one of GHC 8.10, 9.2, or 9.4.

• Ensure that you have the clang and llvm-link utilities from LLVM in your PATH. Currently, LLVM versions up to 16 are supported. LLVM binaries are available at https://github.com/llvm/llvm-project/releases.

• Ensure that you have the z3 SMT solver in your PATH. z3 binaries are available at https://github.com/Z3Prover/z3/releases.

Then, clone the repo and run:

$ git submodule update --init
$ cabal test copilot-verifier

This will clone the repository, build the verifier, and run the associated test suite. If you have performed all of the steps above correctly, the test suite should pass.

We also provide a Dockerfile which automates the process of installing Copilot and the verifier. The Dockerfile can be built and run using the following commands:

$ docker build -t <tag> .
$ docker run -it <tag>

Where <tag> is a unique name for the Docker image.

Using the Copilot Verifier

The main interface to the verifier is the Copilot.Verifier.verify function, which takes some options to drive the code generation process and a Copilot Spec object. It will invoke the Copilot code generator to obtain C sources, compile the C sources into LLVM bitcode using the clang compiler front-end, then parse and interpret the bitcode using crucible. After generating verification conditions, it will dispatch them to an SMT solver, and the result of those queries will be presented to the user.

There are a number of examples (based on similar examples from the main Copilot repository) demonstrating how to incorporate the verifier into a Copilot program. See the copilot-verifier/examples subdirectory of this repository.

Details of the Verification

Synopsis of Copilot semantics

The Copilot DSL represents a certain nicely-behaved class of discrete-time stream programs. Each Stream in Copilot denotes an infinite stream of values; one may just as well think that Stream a represents a pure mathematical function ℕ → a from natural numbers to values of type a. See the Copilot manual for more details of the Copilot language itself and its semantics.

One of the central design considerations for Copilot is that is should be possible to implement stream programs using a fixed, finite amount of storage. As a result, Copilot will only accept stream programs that access a bounded amount of their input streams (including any recursive stream references). This allows an implementation strategy where the generated C code can use fixed-size ring buffers to store a limited number of previous stream values.

The execution model for the generated programs is that the program starts in a state corresponding to stream values at time t = 0; "external" stream input values are placed in distinguished global variables by the calling environment, which then executes a step() function to move to the next time step. The step() function captures the values of these external inputs and does whatever computation is necessary to update its internal state from time t=n to time t=n+1. In addition, it will call any defined "trigger" functions if the stream state at that time step satisfies the defined guard condition. In short, the generated C program steps one moment at a time through the stream program, consuming a sequence of input values provided by a cooperating environment and calling handler functions whenever states of interest occur.

The Desired Correspondence

What does it mean, then, for a generated C program in this style to correctly implement a given stream program? The intuition is basically that after n calls to the step function, the state of the ring-buffers of the C program should correctly compute the value of the corresponding stream expression evaluated at index n, assuming the C program has been fed inputs corresponding to the first n values of the external stream inputs. Moreover, the trigger functions should be called from the step function exactly at the time values when the stream expressions evaluate to true.

The notion of correspondence for the values flowing in streams is relatively straightforward: these values consist of fixed-width machine integers, floating-point values, structs and fixed-length arrays. For each, the appropriate notion of equality is fairly clear.

Both the original Stream program and the generated C program can be viewed straightforwardly as a transition system, and under this view, the correspondence we want to establish is a bisimulation between the states of the high-level stream program and the low-level C program. The proof method for bisimulation requires us to provide a "correspondence" relation between the program states, and then prove three things about this relation:

1. that the initial states of the programs are in the relation;
2. if we assume two arbitrary program states begin in the relation and each takes a single transition (consuming corresponding inputs), the resulting states are back in the relation;
3. that any observable actions taken by one program are exactly mirrored by the other.

On the high-level side of the bisimulation, the program state is essentially just the value of the current time step n, whereas on the C side it consists of the regions of global memory that contain the ring-buffers and their current indices. The transition relation for the high-level program just increments the time value by one, and the transition for the C program is defined by the action of the generated step() function.

Suppose s is one of the stream definitions in the original Copilot program which is required to retain k previous values; let buf be the global variable name of the ring-buffer in the C program, and idx be the global variable name maintaining the current index into the ring buffer. Then the correspondence relation is basically that 0 <= idx < k and s[n+i] = buf[(idx+i) mod k] as i ranges from 0 .. k-1. By abuse of notation, here we mean that s[j] is the value of the stream expression s evaluated at index j, whereas buf[j] means the value obtained by reading the jth value of the buffer buf from memory. The overall correspondence relation is a conjunction of statements like this, one for each stream expression that is realized via a buffer.

Implementing the Bisimulation proof steps

The kind of program correspondence property we desire is a largely mechanical affair. As the code under consideration is automatically generated, it has a very regular structure and is specifically intended to implement the semantics we wich to verify it against. As such, we expect these proofs to be highly automatable.

The proof principle of bisimulation itself is not amenable to reduction to SMT, as if falls outside the first-order theories those solvers understand. Likewise, the semantics of Copilot and C might possibly be reducible directly to SMT, but it would be impractical to do so. However, we can reduce the individual proof obligations mentioned above into a series of lower-level logical statements that are amenable to SMT proof by defining the logical semantics of stream programs, and using symbolic simulation techniques to interpret the semantics of the C program. Performing this reduction is the key contribution of copilot-verifier.

Initial state correspondence

The proof first obligation we must discharge is to show that the initial states of the two programs correspond. For each stream s there is a corresponding k, which is the length of the ring-buffer implementing it. We must simply verify that the C program initializes its buffer with the first k values of the stream s, and that the idx value starts at 0. Because of the restrictions Copilot places on programs, these first k values must be specified concretely and will not be able to depend on any external inputs. As such, this step is quite easily performed, as it requires only direct evaluation of concrete inputs.

Transition correspondence

The bulk of the proof effort consists of demonstrating that the bisimulation relation is preserved by transitions. In order to do this step, we must begin with totally symbolic initial states: we know nothing except that we are at some arbitrary time value n, and that the C program buffers correspond to their stream values as required by the relation. Thus, we create fresh symbolic variables for the streams from n to n + k-1, and for the values of all the involved external streams at time n. Then, we run forward the Copilot program by evaluating the stream recurrence expression to compute the value of each stream at time n+k.

Next we set up an initial state of the C program by choosing, for each ring buffer, an arbitrary value for its current index within its allowed range, and then writing the variables corresponding to each stream value into the buffers at their appropriate offsets. The symbolic simulator is then invoked to compute the state update effects of the step() function. Afterward, we read the poststate values from the ring-buffers and verify that they correspond to the stream values from n+1 up to n+k.

As part of symbolic simulation, Crucible may also generate side-conditions that relate to memory safety of the program, or to error conditions that must be avoided. All of the bisimulation equivalence conditions and the safety side-conditions will be submitted to an SMT solver.

Observable effects

For our purposes, the only observable effects of a Copilot program relate to any "trigger" functions defined in the spec. Our task is to show that the generated C code calls the external trigger functions if and only if the corresponding guard condition is true, and that the arguments passed to those functions are as expected. This proof obligation is proved in the same phase along with the transition relation proof above because the step() function is responsible for both invoking triggers and for performing state updates.

The technique we use to perform this aspect of the proof is to install "override" functions to the external symbols corresponding to the C trigger functions before we begin symbolic simulation. In a real system, the external environment would be responsible for implementing these functions and taking whatever appropriate action is necessary when the triggers fire. However, we are verifying the generated code in isolation from its environment, so we have no implementation in hand. Instead, the override will essentially implement a stub function that simply captures its arguments and the path condition under which it was called. After simulation completes, the captured arguments and path condition are required to be equivalent to the corresponding trigger guard and arguments from the Copilot spec. These conditions are discharged to the SMT solver at the same time as the transition relation obligations.

Because of the way we model the trigger functions, we make a number of implicit assumptions about how the actual implementations of those functions must behave. The most important of those assumptions is that the trigger functions must not modify any memory under the control of the Copilot program, including its ring buffers and stack. We also assume that the trigger functions are well defined, i.e. they are memory safe and do not perform any undefined behavior.

Partial operations

A generated C program may make use of partial operations. These range from division, which can fail if the second argument is zero, to signed integer arithmetic, which can overflow and result in undefined behavior. The verifier has two modes for dealing with partial operations:

1. Any invocation of a partial operation on undefined inputs in the generated C program will result in an error, provided that the user did not add an assertion that assumes this behavior will not occur.

2. If the generated C program invokes a partial operation on undefined inputs, the verifier will check if this coincides with a corresponding invocation of a partial operation in the Copilot spec. If so, the verification will succeed. In other words, the verifier will check that the spec and the C program are "crash-equivalent".

The verifier uses mode (1) by default, but mode (2) can be enabled by using Copilot.Verifier.verifyWithOptions sideCondVerifierOptions. In this mode, the verifier will analyze any invocation of a operation which could be partial and generate a side condition that this operation will only be invoked on well defined inputs. During the transition step of the bisimulation proof, the verifier will add these side conditions as assumptions. Therefore, if simulating the C program generates any side conditions due to invoking partial operations, these side conditions from the C program should be dischargeable using the corresponding side conditions from the Copilot spec.

Mode (2) has the caveat that clang may compile C code to LLVM bitcode in which a partial function is no longer applied to undefined inputs. For instance, clang will sometimes promote 16-bit integer values to 32 bits before performing arithmetic on them. This can turn an operation that would result in signed 16-bit integer overflow into a 32-bit integer operation that does not overflow, for instance.

Caveats About the Verifier

We rely on the clang compiler front-end to consume C source files and produce LLVM intermediate language, which then becomes the input to the later verification steps. To the extent that the input program is not fully-portable C, clang may make implementation-specific decisions about how to compile the program which might be made different if compiled by a different compiler (e.g. gcc). We expect this aspect to be mitigated by the fact that Copilot programs are automatically generated into a rather simple subset of the C language, and is designed to be as simple as possible. Any code-generation bugs in clang itself may affect the soundness of our verifier. Again, however, Copilot generates a well-understood subset of the language, and we expect clang to be well-tested on the code patterns produced.

The semantics of LLVM bitcode, as encoded in the crucible-llvm package, may have errors that affect soundness. We mitigate this risk by testing our semantics against a corpus of verification problems produced for the SV-COMP verification competition, paying special attention to any soundness issues that arise. Crux, a standalone verification system based on crucible-llvm, was a participant in the 2022 edition of SV-COMP.

The semantics of Copilot programs, as encoded in the Copilot.Theorem.What4 module may have errors that affect soundness. For the moment we do not have an effective mitigation strategy for this risk other than manual examination and comparison against the intended semantics of Copilot, as encoded in the interpreter.

There is limited SMT solver support for floating-point values, especially for transcendental functions like the trig primitives. As a result, we reason about floating point expressions via uninterpreted functions. In other words, we leave the semantics of the floating-point operations totally abstract, and simply verify that the Copilot program and the corresponding C program apply the same operations in the same order. This is sound, but leaves the possibility that the compiler will apply some correct transformation to floating-point expressions that we are nonetheless unable to verify. However, on low optimizations and without the --fast-math flag, compilers generally (and clang in particular) are very reluctant to rearrange floating-point code, and the verifications generally succeed.