Awesome Open Source
Awesome Open Source

License MIT


A tool for analyzing LLVM bitcode (generated either by C or C++) using Datalog.

This project uses a commercial Datalog engine, developed by LogicBlox Inc..

System requirements

  • A 64-bit flavor of Linux. Verify that you're running 64-bit Linux by running: uname -m which should return x86_64.
  • At least 4GB of available memory.
  • Python 2.7 or newer (but not Python 3.x). Available from the Python Website
  • Java Developer Kit version 6 or newer. Available from Oracle's Java website

Pre-installation steps

Install the LogicBlox engine

The LogicBlox engine needs to be installed. We recommend the PA-Datalog engine, which is a modified LogicBlox v3 engine, intended for use in program analysis projects.

(Alternatively, you can download a full-fledged LogicBlox engine (version 3.*) from the LogicBlox Download Page. You will need to [request an academic license] (

You must also set the environment variable $LOGICBLOX_HOME and augment your $PATH accordingly. The following additions to either your .bashrc or .bash_profile should suffice, assuming that you have extracted the engine to /opt/lb/. If not, adjust the following lines appropriately:

export LOGICBLOX_HOME=/opt/lb/logicblox-3.10.14/logicblox

Install LLVM

This step is not needed for newer Linux distributions, where you can install LLVM version 3.7 (or later) from the system's package manager.

  • Download LLVM 3.7.0 pre-built binary from the LLVM Download Page.
  • Untar the downloaded file to a destination path of your choice (e.g., /opt/llvm/) and modify permissions accordingly.
  • Add the /path/to/llvm-3.7.0/bin to your $PATH (by modifying your .bashrc or .bash_profile).

Additional Libraries

You will also have to install the following packages:

Fedora 20, 21, 22

# yum install boost-devel boost-python protobuf-devel python-pip python-devel

Fedora 24

# dnf install boost-devel boost-python protobuf-devel python-pip python-devel
# dnf install llvm-devel clang-devel


# apt-get install build-essential libboost-dev libboost-filesystem-dev libboost-program-options-dev libboost-python-dev libprotobuf-dev libprotoc-dev protobuf-compiler python-pip python-dev

Ubuntu 15.10

In latest distro versions, that have switched to gcc 5, the binary compatibility between clang and gcc is broken (see bug 23529). So, the pre-built LLVM binaries will not work there.

Instead, for Ubuntu 15.10, you can:

  1. Skip the pre-built binary download step entirely, but otherwise follow the (Ubuntu) instructions

  2. Additionally install LLVM 3.7 and libedit from the system's package manager by running:

     # apt-get install llvm-3.7 libedit-dev
  3. When compiling the project, run make as follows:

     (venv)$ LLVM_CONFIG=llvm-config-3.7 make
     (venv)$ make install

YAML Configuration

To be able to easily customize your analysis via a configuration file, you will also need to install the python-yaml package.

The default user configuration will be automagically installed at ~/.config/cclyzer/config.yaml the first time you run the tool. Then, you can tweak this config file, e.g., to change the printed statistics and the loaded logic modules.


We recommend first to create a virtual environment by running:

$ pip install virtualenv  # if not already installed
$ cd /path/to/cclyzer/
$ virtualenv venv

To activate the virtual environment, run:

$ . venv/bin/activate
(venv)$    # <--- your prompt should change to something like this

Now, while inside the virtualenv, build cclyzer as follows:

(venv)$ make
(venv)$ make install

Then, you should be able to run the main cclyzer script that analyzes LLVM Bitcode. Try:

(venv)$ cclyzer -h
(venv)$ cclyzer analyze -h


The basic test suite comprises the GNU Core Utilities.

You may run all the tests with:

$ make

or a particular test, e.g., stty, with:

$ make test-stty

It is also possible to invoke a python interpreter for a more interactive experience:

$ python
>>> from cclyzer import *
>>> config = AnalysisConfig('./tests/coreutils-8.24/sort.bc', output_dir='./build/tests/sort')
>>> analysis = Analysis(config)
>>> print analysis.stats
# instructions        : 25417
# functions           :   438
# app functions       :   317


The warnings and errors that may come up during execution are not very informative. Instead, the log file located at $XDG_CACHE_HOME/cclyzer/cclyzer.log (which at most systems defaults to ~/.cache/cclyzer/cclyzer.log), or the system log, can be much more helpful.

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