A verified and optimized Stream X-Machine testing method, with application to cloud service certification

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 optimisation steps achieve up to a ten-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 spec-ification, 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

Implementation relations and testing for cyclic systems: adding probabilities

This paper concerns the systematic testing of robotic control software based on state-based models. We focus on cyclic systems that typically receive inputs (values from sensors), perform computations, produce outputs (sent to actuators) and possibly change state. We provide a testing theory for such cyclic systems where time can be represented and probabilities are used to quantify non-deterministic choices, making it possible to model probabilistic algorithms. In addition, refusals, the inability of a system to perform a set of actions, are taken into account. We consider several possible testing scenarios. For example, a tester might only be able to passively observe a sequence of events and so cannot check probabilities, while in another scenario a tester might be able to repeatedly apply a test case and so estimate the probabilities of sequences of events. These different testing scenarios lead to a range of implementation relations (notions of correctness). As a consequence, this paper provides formal definitions of implementation relations that can form the basis of sound automated testing in a range of testing scenarios. We also validate the implementation relations by showing how observers can be used to provide an alternative but equivalent characterisation

Kernel P systems: from modelling to verification and testing

YesA kernel P system integrates in a coherent and elegant manner some of the most successfully used features of the P systems employed in modelling various applications. It also provides a theoretical framework for analysing these applications and a software environment for simulating and verifying them. In this paper, we illustrate the modelling capabilities of kernel P systems by showing how other classes of P systems can be represented with this formalism and providing a number of kernel P system models for a sorting algorithm and a broadcasting problem. We also show how formal verification can be used to validate that the given models work as desired. Finally, a test generation method based on automata is extended to non-deterministic kernel P systems.The work of MG, FI and RL were supported by a grant of the Romanian National Author- ity for Scientific Research, CNCS-UEFISCDI (project number: PN-II-ID-PCE-2011-3-0688); RCU

Testing Identifiable Kernel P Systems Using an X-machine Approach

This paper presents a testing approach for kernel P systems (kP systems), based on the X-machine testing framework and the concept of cover automaton. The testing methodology ensures that the implementation conforms the speci cations, under certain conditions, such as the identi ably concept in the context of kernel P systems

Why the Virtual Nature of Software Makes It Ideal for Search Based Optimization

This paper(1) provides a motivation for the application of search based optimization to Software Engineering, an area that has come to be known as Search Based Software Engineering (SBSE). SBSE techniques have already been applied to many problems throughout the Software Engineering lifecycle, with new application domains emerging on a regular basis. The approach is very generic and therefore finds wide application in Software Engineering. It facilitates automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. Previous work has already discussed, in some detail, the advantages of the SBSE approach for Software Engineering. This paper summarises previous work and goes further, by arguing that Software Engineering provides the ideal set of application problems for which optimization algorithms are supremely well suited