9e7363bd27
`re.loop` has two accepted AST forms: bounds as decl parameters, `((_ re.loop lo hi) r)` (1 argument, 2 parameters), and bounds as arguments, `(re.loop r lo hi)` (3 arguments, 0 parameters). The plugin accepts arities 1, 2 and 3, and `is_loop` has separate overloads for each form. Only the parameter form was recognized downstream. ### The soundness bug `mk_info_rec`'s `OP_RE_LOOP` case read the bounds solely from `get_decl()->get_num_parameters()`, which is 0 for the argument form, so it silently fell back to the locals `lower_bound = 0, upper_bound = UINT_MAX`. `info::loop` computes ```cpp loop_nullable = (nullable == l_true || lower == 0) ? l_true : nullable; ``` so `lower == 0` forces nullability, and `min_length` collapses to `min_length * 0 = 0`. Net effect: `(re.loop (str.to_re "ab") 1 3)` was reported as `nullable = l_true, min_length = 0`. At the SMT level ```smt2 (declare-const x String) (assert (str.in_re x (re.loop (str.to_re "ab") 1 3))) (assert (= (str.len x) 0)) (check-sat) ``` answered **sat** rather than unsat. The indexed form of the same regex correctly answers unsat. `seq_rewriter::mk_re_loop` already normalizes the argument form to `mk_loop_proper`, which is why this was never observable on the usual paths — it only surfaces where the rewriter is bypassed, as in `seq_monadic`. The `info.nullable` fast path added in #10366 didn't create the bug but widened the path that reaches it. ### The performance bug `derive_core` had no case for the argument form either, so the derivative got stuck as an uninterpreted `re.derivative` term. Instrumenting all three sites that can emit a symbolic `re.derivative` showed that **every one of the 4171 stuck terms** in the regex corpus came from this fall-through (the 512-deep recursion cap and the unnormalizable `re.reverse` site never fired), and 4096 of them came from a single legacy-form loop, whose search then burned its full budget before giving up. ### The fix * `mk_info_rec` branches on `get_num_args()`; for the argument form it reads the bounds from `get_arg(1)`/`get_arg(2)` when they are unsigned numerals, and returns `unknown_info` when they are not. * `derive_core` normalizes the numeral argument forms to `mk_loop_proper`/`mk_loop` and recurses. The block is gated on the cheap `re().is_loop(r)` kind check so that regexes falling through to the later cases don't pay for the `rational` locals. Arguably the right long-term place to normalize is the parser, so the legacy form never reaches the AST at all. Keeping the `mk_info_rec` half is still worthwhile as a guard for API-constructed terms, and the regression test builds the term directly via `mk_app`, so it stays meaningful either way. ### Regression test `src/test/seq_rewriter.cpp` gains a case asserting that `get_info` agrees on the two forms (`nullable == l_false`, `min_length == 2`). Verified to actually catch the bug: reverting `seq_decl_plugin.cpp` and rebuilding gives `ASSERTION VIOLATION` with `nullable=l_true min_length=0`. ### Evaluation Full run over 1545 regex benchmarks with declared statuses, in both transition modes: | | light-ant | brz | |---|---|---| | decided (before → after) | 1462 → **1463** | 1464 → **1465** | | soundness mismatches | 0 → 0 | 0 → 0 | | verdict changes | 1 | 1 (same file) | The one changed file is `L4-01-loop-sat.smt2`: **undef @ 8625 ms → sat @ 0.20 ms**. The corpus contains 1 legacy-form file and 236 indexed-form files, which share the modified `OP_RE_LOOP` path, hence the full A/B rather than a spot check. Timing is neutral. Measured with an interleaved base/fix/base/fix protocol over two saved binaries, since same-binary run-to-run variance (±8%) turned out to exceed the effect: paired best-of-2 over the 315 files taking >1 ms gives a **median ratio of 0.998** (p25/p75 = 0.889/1.075), overall 0.976. `tst_seq_rewriter` passes, `seq_monadic` is ALL PASS in both modes, and the 22172-file QF_S corpus shows no verdict changes and no mismatches. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: Nikolaj Bjorner <nikolaj@cs.stanford.edu> Copilot-Session: a2ce3573-4e15-4a4a-afb5-21e3cb04e4a2 |
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Z3
Z3 is a theorem prover from Microsoft Research. It is licensed under the MIT license. Windows binary distributions include C++ runtime redistributables
If you are not familiar with Z3, you can start here.
Pre-built binaries for stable and nightly releases are available here.
Z3 can be built using Visual Studio, a Makefile, using CMake, using vcpkg, or using Bazel. It provides bindings for several programming languages.
See the release notes for notes on various stable releases of Z3.
Build status
Pull Request & Push Workflows
| WASM Build | Windows Build | CI | OCaml Binding |
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Scheduled Workflows
| Open Bugs | Android Build | Pyodide Wheel (PyPI) | Nightly Build | Cross Build | F* Master Build |
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| MSVC Static | MSVC Clang-CL | Build Z3 Cache | Memory Safety | Mark PRs Ready |
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Manual & Release Workflows
| Documentation | Release Build | WASM Release |
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Specialized Workflows
| Nightly Validation | Copilot Setup | Agentics Maintenance |
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Agentic Workflows
| API Coherence | Code Simplifier | Release Notes | Workflow Suggestion | Academic Citation |
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| Issue Backlog | Memory Safety Report | Specbot Crash Analyzer | SMTLIB Benchmark Finder |
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Building Z3 on Windows using Visual Studio Command Prompt
For 32-bit builds, start with:
python scripts/mk_make.py
or instead, for a 64-bit build:
python scripts/mk_make.py -x
then run:
cd build
nmake
Z3 uses C++20. The recommended version of Visual Studio is therefore VS2019 or later.
Security Features (MSVC): When building with Visual Studio/MSVC, a couple of security features are enabled by default for Z3:
- Control Flow Guard (
/guard:cf) - enabled by default to detect attempts to compromise your code by preventing calls to locations other than function entry points, making it more difficult for attackers to execute arbitrary code through control flow redirection - Address Space Layout Randomization (
/DYNAMICBASE) - enabled by default for memory layout randomization, required by the/GUARD:CFlinker option - These can be disabled using
python scripts/mk_make.py --no-guardcf(Python build) orcmake -DZ3_ENABLE_CFG=OFF(CMake build) if needed
Building Z3 using make and GCC/Clang
Execute:
python scripts/mk_make.py
cd build
make
sudo make install
Note by default g++ is used as C++ compiler if it is available. If you
prefer to use Clang, change the mk_make.py invocation to:
CXX=clang++ CC=clang python scripts/mk_make.py
Note that Clang < 3.7 does not support OpenMP.
You can also build Z3 for Windows using Cygwin and the Mingw-w64 cross-compiler. In that case, make sure to use Cygwin's own Python and not some Windows installation of Python.
For a 64-bit build (from Cygwin64), configure Z3's sources with
CXX=x86_64-w64-mingw32-g++ CC=x86_64-w64-mingw32-gcc AR=x86_64-w64-mingw32-ar python scripts/mk_make.py
A 32-bit build should work similarly (but is untested); the same is true for 32/64 bit builds from within Cygwin32.
By default, it will install z3 executables at PREFIX/bin, libraries at
PREFIX/lib, and include files at PREFIX/include, where the PREFIX
installation prefix is inferred by the mk_make.py script. It is usually
/usr for most Linux distros, and /usr/local for FreeBSD and macOS. Use
the --prefix= command-line option to change the install prefix. For example:
python scripts/mk_make.py --prefix=/home/leo
cd build
make
make install
To uninstall Z3, use
sudo make uninstall
To clean Z3, you can delete the build directory and run the mk_make.py script again.
Building Z3 using CMake
Z3 has a build system using CMake. Read the README-CMake.md file for details. It is recommended for most build tasks, except for building OCaml bindings.
Building Z3 using vcpkg
vcpkg is a full platform package manager. To install Z3 with vcpkg, execute:
git clone https://github.com/microsoft/vcpkg.git
./bootstrap-vcpkg.bat # For powershell
./bootstrap-vcpkg.sh # For bash
./vcpkg install z3
Building Z3 using Bazel
Z3 can be built using Bazel. This is known to work on Ubuntu with Clang (but may work elsewhere with other compilers):
bazel build //...
Dependencies
Z3 itself has only few dependencies. It uses C++ runtime libraries, including pthreads for multi-threading. It is optionally possible to use GMP for multi-precision integers, but Z3 contains its own self-contained multi-precision functionality. Python is required to build Z3. Building Java, .NET, OCaml and Julia APIs requires installing relevant toolchains.
Z3 bindings
Z3 has bindings for various programming languages.
.NET
You can install a NuGet package for the latest release Z3 from nuget.org.
Use the --dotnet command line flag with mk_make.py to enable building these.
See examples/dotnet for examples.
C
These are always enabled.
See examples/c for examples.
C++
These are always enabled.
See examples/c++ for examples.
Java
Use the --java command line flag with mk_make.py to enable building these.
For IDE setup instructions (Eclipse, IntelliJ IDEA, Visual Studio Code) and troubleshooting, see the Java IDE Setup Guide.
See examples/java for examples.
Go
Use the --go command line flag with mk_make.py to enable building these. Note that Go bindings use CGO and require a Go toolchain (Go 1.20 or later) to build.
With CMake, use the -DZ3_BUILD_GO_BINDINGS=ON option.
See examples/go for examples and src/api/go/README.md for complete API documentation.
OCaml
Use the --ml command line flag with mk_make.py to enable building these.
See examples/ml for examples.
Python
You can install the Python wrapper for Z3 for the latest release from pypi using the command:
pip install z3-solver
Use the --python command line flag with mk_make.py to enable building these.
Note that it is required on certain platforms that the Python package directory
(site-packages on most distributions and dist-packages on Debian-based
distributions) live under the install prefix. If you use a non-standard prefix
you can use the --pypkgdir option to change the Python package directory
used for installation. For example:
python scripts/mk_make.py --prefix=/home/leo --python --pypkgdir=/home/leo/lib/python-2.7/site-packages
If you do need to install to a non-standard prefix, a better approach is to use
a Python virtual environment
and install Z3 there. Python packages also work for Python3.
Under Windows, recall to build inside the Visual C++ native command build environment.
Note that the build/python/z3 directory should be accessible from where Python is used with Z3
and it requires libz3.dll to be in the path.
virtualenv venv
source venv/bin/activate
python scripts/mk_make.py --python
cd build
make
make install
# You will find Z3 and the Python bindings installed in the virtual environment
venv/bin/z3 -h
...
python -c 'import z3; print(z3.get_version_string())'
...
See examples/python for examples.
Julia
The Julia package Z3.jl wraps the C API of Z3. A previous version of it wrapped the C++ API: Information about updating and building the Julia bindings can be found in src/api/julia.
WebAssembly / TypeScript / JavaScript
A WebAssembly build with associated TypeScript typings is published on npm as z3-solver. Information about building these bindings can be found in src/api/js.
Smalltalk (Pharo / Smalltalk/X)
Project MachineArithmetic provides a Smalltalk interface to Z3's C API. For more information, see MachineArithmetic/README.md.
AIX
Build settings for AIX are described here.
System Overview
Interfaces
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Default input format is SMTLIB2
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Other native foreign function interfaces:
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Python API (also available in pydoc format)
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C
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OCaml
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Smalltalk (supports Pharo and Smalltalk/X)
Power Tools
- The Axiom Profiler currently developed by ETH Zurich