Static Program Analysis
Anders Møller and Michael I. Schwartzbach Last revision: January 2017 
Static program analysis is the art of reasoning about the behavior of computer programs without actually running them. This is useful not only in optimizing compilers for producing efficient code but also for automatic error detection and other tools that can help programmers.
As known from Turing and Rice, all interesting properties of the behavior of programs written in common programming languages are mathematically undecidable. This means that automated reasoning of software generally must involve approximation. It is also well known that testing may reveal errors but not show their absence. In contrast, static program analysis can  with the right kind of approximations  check all possible executions of the programs and provide guarantees about their properties. The challenge when developing such analyses is how to ensure high precision and efficiency to be practically useful.
This teaching material concisely presents the essential principles and algorithms for static program analysis. We emphasize a constraintbased approach where suitable constraint systems conceptually divide analysis into a frontend that generates constraints from program code and a backend that solves the constraints to produce the analysis results. The style of presentation is intended to be precise but not overly formal. The readers are assumed to be familiar with advanced programming language concepts and the basics of compiler construction.
The concepts are explained using a tiny imperative programming language, TIP, which suffices to illustrate the main challenges that arise with mainstream languages.
The lecture notes, slides, implementation, and exercises have been developed since 2008 for our graduatelevel course at Aarhus University. We continue to update the material regularly. Suggestions for improvements are welcome!
Topics covered:
The analyses are expressed using different kinds of constraint systems, each with their own constraint solvers:
Lecture slides:
Most of the algorithms and analyses have been implemented (in Scala):
the TIP implementation at github (scaladoc)
(developed by Gianluca Mezzetti and Anders Møller, with contributions from Erik Krogh Kristensen, Christian Budde Christensen, Coen De Roover, and Quentin Stievenart)
The open source version omits certain parts that are left for exercises. Solutions for (most of) these programming exercises are available to teachers by request.
Our TODO list:
Feel free to contact the authors if you have any questions or comments to the material.
