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JFS (Just Fuzz it Solver) (originally JIT Fuzzing Solver) is an experimental constraint solver designed to investigate using coverage guided fuzzing as an incomplete strategy for solving boolean, BitVector, and floating-point constraints.

JFS supports constraints in the SMT-LIBv2 constraint langauge in the QF_BV, QF_BVFP, and QF_FP logics. JFS's primary purpose however is solve floating-point constraints.

JFS is built on top of the following projects

FSE 2019 paper

A paper on JFS was presented and published at ESEC/FSE 2019.

Additional resources:

The Docker image with the tag fse_2019

Using JFS Docker image

If you want to get started with JFS with minimal effort the easiest thing to do is download the latest Docker image for JFS. Note this image may not be up-to-date so you might not the latest changes.

To obtain the image run (replace fse_2019 with the tag you wish to use).

docker pull delcypher/jfs_build:fse_2019

The simplest invocation is something like this which will show JFS's help output.

docker run --user 1000 --rm -t delcypher/jfs_build:fse_2019 /home/user/jfs/build/bin/jfs --help

To run JFS on a SMT-LIBv2 file that exists outside the container (/path/to/simple.smt2 in this example) run this command below.

docker run --rm -t -v /path/to/simple.smt2:/tmp/simple.smt2 delcypher/jfs_build:fse_2019 /home/user/jfs/build/bin/jfs /tmp/simple.smt2

Note that the docker run command creates a new container and destroys it afterwards which has notable overhead. For better performance consider running JFS outside of a docker container or creating a single container and spawning a shell inside it and then launch JFS from there.

Building JFS

JFS has been tested on Linux and macOS. Windows support would likely require a lot more work and is dependent on getting LibFuzzer to work on Windows.

Using Docker (the easy way)

The easiest way is just to use our Dockerfile. To do this simply run


once the script completes you will have a Docker image on your system named jfs_build:ubuntu1604. In this image you will find the JFS binaries in the /home/user/jfs/build/bin directory.

From source (the hard way)

JFS has the following build dependencies:

  • LLVM/Clang/compiler-rt 6.0
  • Z3 4.6.0
  • CMake
  • Ninja

Here are the steps to build JFS.

  1. Build Z3 4.6.0. Note you must build this using Z3's CMake build system and not its legacy build system because JFS's build system depends on files emitted by Z3's CMake build system.

A convenience script is provided for this

export Z3_SRC_DIR=/home/user/z3/src
export Z3_BUILD_DIR=/home/user/z3/build
export Z3_BUILD_TYPE=Release

Set the Z3_SRC_DIR, Z3_BUILD_DIR to paths to empty or non-existant directories. The Z3_BUILD_TYPE can be set to Release, RelWithDebInfo, or Debug.

  1. Build or install LLVM, Clang, and compiler-rt 6.0

A convenience script is provided to build LLVM.

export LLVM_SRC_DIR=/home/user/llvm/src
export LLVM_BUILD_DIR=/home/user/llvm/src
export LLVM_BUILD_TYPE=Release

Set the LLVM_SRC_DIR, LLVM_BUILD_DIR to paths to empty or non-existant directories. The LLVM_BUILD_TYPE can be set to Release, RelWithDebInfo, or Debug.

  1. Build JFS

A convenience script is provided to build JFS.

export JFS_SRC_DIR=/home/user/jfs/src
export JFS_BUILD_DIR=/home/user/jfs/build
export JFS_BUILD_TYPE=Release

JFS_SRC_DIR should be the absolute path to an already cloned copy of the JFS repo. JFS_BUILD_DIR should be a path to an empty or non-existant directory. The JFS_BUILD_TYPE can be set to Release, RelWithDebInfo, or Debug.

Note that Z3_BUILD_DIR and LLVM_BUILD_DIR must also be set.

  1. Test JFS
ninja check


Aren't there already a bunch of search based floating-point constraint solvers?

Yes. However as far as we're aware, JFS is the first to try to use an "off the shelf" coverage guided fuzzer as a search strategy.

Here's a non-exhaustive list:

  • coral is a constraint solver that supports various search strategies over floating point constraints. It uses its own constraint language which is only partially overlaps with QF_FP constraints in the SMT-LIBv2 language. The smt2coral tool provides a way to run coral on SMT-LIBv2 constraints.

  • XSat transform constraints into a mathematical global optimization problem. Unfortunately this tool is not open source.

  • goSAT takes the ideas behind XSat but uses different optimization libraries and uses LLVM's JIT to generate code.

We are currently in the process of performing an evaluation of JFS against these solvers and we will report these results in the not to distant future.

How does JFS work?

To see how JFS works let's walk through a small example.

  1. Parse SMT-LIB constraints using Z3.
(declare-fun a () (_ FloatingPoint 11 53))
(declare-fun b () (_ FloatingPoint 11 53))
(define-fun a_b_rne () (_ FloatingPoint 11 53) (fp.div RNE a b))
(define-fun a_b_rtp () (_ FloatingPoint 11 53) (fp.div RTP a b))
(assert (not (fp.isNaN a)))
(assert (not (fp.isNaN b)))
(assert (not (fp.eq a_b_rne a_b_rtp)))
(assert (not (fp.isNaN a_b_rne)))
(assert (not (fp.isNaN a_b_rtp)))
  1. Perform some simplifications on the constraints (e.g. constant folding).

NOTE: You can use the jfs-opt tool to experiment with these simplifications.

  1. Generate a C++ program where the reachability of an abort() statement is equivalent to finding a satisfying assignment to the constraints.

NOTE: You can use the jfs-smt2cxx tool to convert SMT-LIBv2 constraints into a program.

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
  if (size < 16) {
    return 0;
  BufferRef<const uint8_t> jfs_buffer_ref =
      BufferRef<const uint8_t>(data, size);
  const Float<11, 53> a = makeFloatFrom<11, 53>(jfs_buffer_ref, 0, 63);
  const Float<11, 53> b = makeFloatFrom<11, 53>(jfs_buffer_ref, 64, 127);
  const bool jfs_ssa_0 = a.isNaN();
  const bool jfs_ssa_1 = !(jfs_ssa_0);
  if (jfs_ssa_1) {
  } else {
    return 0;
  const bool jfs_ssa_2 = b.isNaN();
  const bool jfs_ssa_3 = !(jfs_ssa_2);
  if (jfs_ssa_3) {
  } else {
    return 0;
  const Float<11, 53> jfs_ssa_4 = a.div(JFS_RM_RNE, b);
  const Float<11, 53> jfs_ssa_5 = a.div(JFS_RM_RTP, b);
  const bool jfs_ssa_6 = jfs_ssa_4.ieeeEquals(jfs_ssa_5);
  const bool jfs_ssa_7 = !(jfs_ssa_6);
  if (jfs_ssa_7) {
  } else {
    return 0;
  const bool jfs_ssa_8 = jfs_ssa_4.isNaN();
  const bool jfs_ssa_9 = !(jfs_ssa_8);
  if (jfs_ssa_9) {
  } else {
    return 0;
  const bool jfs_ssa_10 = jfs_ssa_5.isNaN();
  const bool jfs_ssa_11 = !(jfs_ssa_10);
  if (jfs_ssa_11) {
  } else {
    return 0;
  // Fuzzing target
  1. This program is then compiled by Clang with coverage instrumentation and linked against LibFuzzer and a small runtime library. The runtime library implements the Float and BitVector SMT-LIBv2 types.

  2. A set of seeds are generated for the fuzzer.

  3. The compiled binary is invoked with the given seeds. If the fuzzer generates an input that reaches the abort() a satisfying assignment has been found and JFS terminates reporting sat.

Note this strategy will never find a satisfying assignment if one does not exist (i.e. the constraints are unsatisfiable). This is why we say JFS is "incomplete" because it cannot show that unsatisfiable constraints are unsatisfiable.

There is one exception to this. JFS can show unsatisfiable constraints to be unsatisfiable if its simplications show one or more constraints to be false (i.e. trivially unsatisfiable).