AIGEN: Random Generation of Symbolic Transition Systems

Jacobs, Swen and Sakr, Mouhammad
(2021) AIGEN: Random Generation of Symbolic Transition Systems.
In: 33rd International Conference on Computer-Aided Verification.
Conference: CAV Computer Aided Verification
(In Press)

[img] Text
camera-ready.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (673kB)

Abstract

AIGEN is an open source tool for the generation of tran- sition systems in a symbolic representation. To ensure diversity, it employs a uniform random sampling over the space of all Boolean functions with a given number of variables. AIGEN relies on reduced ordered binary decision diagrams (ROBDDs) and canonical disjunctive normal form (CDNF) as canonical representations that allow us to enumerate Boolean functions, in the former case with an encoding that is inspired by data structures used to implement ROBDDs. Several parameters allow the user to restrict generation to Boolean functions or transition systems with certain properties, which are then output in AIGER format. We report on the use of AIGEN to generate random benchmark problems for the reactive synthesis competition SYNTCOMP 2019, and present a comparison of the two encodings with respect to time and memory efficiency in practice.

Actions

Actions (login required)

View Item View Item