A model learning based testing approach for kernel P systems

Abstract

Kernel P systems have been introduced as a unifying formalism allowing to specify, simulate and analyse various problems. Several applications of this model have been considered and a powerful tool built in order to support their development and analysis. Testing represents an important aspect of any system analysis and correctness. In this paper we introduce for the first time a bounded test generation approach for kernel P systems by considering bounded input sequences. A learning algorithm for kernel P systems is based on learning X-machine models that are equivalent to these systems for sequences of steps up to a certain limit, ℓ. The Lℓ learning algorithm is used. The testing approach is then devised from the inferred X-machines. The method is applied to a case study illustrating the key parts of the approach.