From Temporal Models to Property-Based Testing

This paper presents a framework to apply property-based testing (PBT) on top of temporal formal models. The aim of this work is to help software engineers to understand temporal models that are presented formally and to make use of the advantages of formal methods: the core time-based constructs of a formal method are schematically translated to the BeSpaceD extension of the Scala programming language. This allows us to have an executable Scala code that corresponds to the formal model, as well as to perform PBT of the models functionality. To model temporal properties of the systems, in the current work we focus on two formal languages, TLA+ and FocusST.Comment: Preprint. Accepted to the 12th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2017). Final version published by SCITEPRESS, http://www.scitepress.or

Spatio-temporal architecture-based framework for testing services in the cloud

Increasingly, various services are deployed and orchestrated in the cloud to form global, large-scale systems. The global distribution, high complexity, and physical separation pose new challenges into the quality assurance of such complex services. One major challenge is that they are intricately connected with the spatial and temporal characteristics of the domains they support. In this paper, we present our visions on the integration of spatial and temporal logic into the system design and quality maintenance of the complex services in the cloud. We suggest that new paradigms should be proposed for designing software architecture that will particularly embed the spatial and temporal properties of the cloud services, and new testing methodologies should be developed based on architecture including spatio-temporal aspects. We also discuss several potential directions in the relevant research

Formal model for intelligent route planning

This paper presents an approach towards intelligent route planning in public transport systems. The approach focuses on formal modelling of the semi-dynamic intelligent route planning and optimisation. For these purposes, it is essential to have a well developed formal model covering real-time and space aspects. The proposed solution allows designers to extend a public transport system with additional routes, which are created dynamically based on the requests from passengers. The model can be applied within a sustainable Smart City both for (fully or partially) autonomous transport systems and for the decision support systems of a smart transport system

Performance comparison of functional and effective brain connectivity methods

Functional and effective connectivity estimates based on electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to understand and reveal new insight into the dynamic behaviour of the brain. However, with a large number of different connectivity methods that are currently available, there is a lack of systematic comparative studies including a statistical evaluation of their performance to understand the strengths and shortcomings of competing methods. Here, we present a simulation framework to evaluate and compare the performance of connectivity estimators on simulated, yet realistic electromagnetic recordings. We assess the ability of various methods to reconstruct cortical networks, while systematically varying specific parameters which are of significant importance during the simulation, preprocessing or inverse source reconstruction of realistic EEG recordings. A decisive advantage of this simulation framework, when compared with models utilised in other studies, is the integration of volume conduction artifacts. This is achieved by modelling the propagation of electric or magnetic fields from an electric primary current source through biological tissue towards measurement sensors. Subsequently, inverse source reconstruction approaches are applied to estimate the temporal activity patterns of underlying network nodes. The implementation of these concepts enabled the analysis of parameters involved during forward modelling and source reconstruction which may affect the estimation of connectivity on the source level. The experiments carried out in this work unfold the behaviour of estimators regarding the effect of signal-to-noise ratio (SNR), length of data sets, various phase shifts between correlated signals, the impact of regularization used in inverse source reconstruction, errors in the localization and varying network sizes. For each simulation, strengths and weaknesses of methods are pointed out. Furthermore, pitfalls and obstacles researchers might come across when applying particular estimators on EEG recordings are discussed. Building on the insight gained from simulation studies, the final part of the thesis analyses the performance of connectivity estimators when applied to resting-state EEG recordings. Network reconstructions with priority on the alpha frequency band reveal a default-mode-network (DMN) with dominant posterior-to-anterior information flow. We detected no significant variations in the amount of correctly identified network links between connectivity methods. However, we discuss differences in connectivity spectra that emerged, which affect the interpretability and applicability of methods