The latest version of PhASAR v1218 is now available. Please check https://github.com/secure-software-engineering/phasar/wiki/PhASAR’s-Releases for details. Among other features, we added infrastructure for backward control-flow graphs. These graphs can be used in order to solve backward data-flow problems.
Today, we will release a minor update (1018) that includes various bug fixes, improved code quality, as well as some minor changes in the API (mostly renaming).
In addition, we will switch to a monthly release plan to avoid expensive, breaking changes and to keep our users up-to-date.
Next week we will release a major update that includes various bug fixes, faster compiles, heavy performance optimizations and novel features. To take just one small example, we were able to reduce the overall runtime of a complete analysis run on clang from several hours to several minutes.
The slides and the analysis code of our taint analysis toy-example, that we presented at our tutorial, are now available at phasar.org/download.
We have prepared the material for our tutorial given @PLDI 2018. If you participate in the tutorial, we kindly ask you to install Phasar on your machine before the tutorial, so we can get right into working with it from the start in order to maximize your learning experience. As many C++ projects Phasar does have a considerable compile time and we would like you to be productive from the very beginning and not to wait for the compiler to be done. Also some of our provided pre-packaged options for the tutorial are quite large, so we would like you to download and install your preferred option ahead of time.
There are three options to work with Phasar during the tutorial:
1. Using a VirtualBox VM
2. Using a Docker container
3. With a cloned copy of the source code
We are looking forward to an interesting tutorial with you.
We will present the Phasar framework at the PLDI 2018 conference held in Philadelphia, Pennsylvania, United States.
We have implemented Phasar, a novel static-analysis framework on top of LLVM. Phasar provides various solvers that allow the solving of arbitrary monotone data-flow problems (distributive or not) in a fully automated manner. A user just has to provide the specific description of the problem to solve.
Phasar’s solver supports a context and flow-sensitive analysis, which is notoriously hard to scale.
Also, the C and C++ programming languages make it especially hard to statically analyze. This is due to their deliberately unsafe type system, function pointers and in case of C++ virtual dispatch and other powerful language constructs.
We will explain how Phasar uses summaries, tabulation and a compositional analysis approach to nevertheless scale to actual applications with millions of lines of code.
We will give an introduction to the framework, explain its relationship to LLVM, its architecture, and give hands-on examples of how to implement useful example analyses with Phasar. For the examples we will focus on security applications.
Before the tutorial, we will release Phasar as a well documented open-source project on Github.