Test data generation for event-b models using genetic algorithms

Abstract

Event-B is a formal modeling language having set theory as its mathematical foundation and abstract state machines as its behavioral specifications. The language has very good tool support based on theorem proving and model checking technologies, but very little support for test generation. Motivated by industrial interest in the latter domain, this paper presents an approach based on genetic algorithms that generates test data for Event-B test paths. For that, new fitness functions adapted to the set-theoretic nature of Event-B are devised. The approach was implemented and its efficiency was proven on a carefully designed benchmark using statistically sound evaluations. © 2011 Springer-Verlag.