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.

Summary The Stream X‐Machine (SXM) testing method provides strong and repeatable guarantees of functional correctness, up to a specification. These qualities make the method attractive for software certification, especially in the domain of brokered cloud services, where arbitrage seeks to substitute functionally equivalent services from alternative providers. However, practical obstacles include the difficulty in providing a correct specification, the translation of abstract paths into feasible concrete tests and the large size of generated test suites. We describe a novel SXM verification and testing method, which automatically checks specifications for completeness and determinism, prior to generating complete test suites with full grounding information. Three optimization steps achieve up to a 10‐fold reduction in the size of the test suite, removing infeasible and redundant tests. The method is backed by a set of tools to validate and verify the SXM specification, generate technology‐agnostic test suites and ground these in SOAP, REST or rich‐client service implementations. The method was initially validated using seven specifications, three cloud platforms and five grounding strategies.

The application of metaheuristic search techniques in test data generation has been extensively investigated in recent years. Most studies, however, have concentrated on the application of such techniques in structural testing. The use of search-based techniques in functional testing is less frequent, the main cause being the implicit nature of the specification. This paper investigates the use of search-based techniques for functional testing, having the specification in form of a state machine. Its purpose is to generate input data for chosen paths in a state machine, so that the parameter values provided to the methods satisfy the corresponding guards and trigger the desired transitions. A general form of a fitness function for an individual path is presented and this approach is empirically evaluated using three search techniques: simulated annealing, genetic algorithms and particle swarm optimization.