Taxonomy-based Annotations for Variability Management

Currently, variability management in software product lines requires novel mechanisms to deal with the inherent complexity of domain modeling. From this perspective, the construction of semantic artifacts, supporting the modeling and implementation of variability from users’ requirements to reuse component development, gives stakeholders a framework for communication and disambiguation. Our work is based on level-domain views and driven by taxonomy-based annotations for describing variability and commonality. We illustrate the proposal through a case study in the marine ecology domain, where results showed an improvement in development time.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model

An Experimental Scrutiny of Visual Design Modelling: VCL up against UML+OCL

The graphical nature of prominent modelling notations, such as the standards UML and SysML, enables them to tap into the cognitive benefits of diagrams. However, these notations hardly exploit the cognitive potential of diagrams and are only partially graphical with invariants and operations being expressed textually. The Visual Contract Language (VCL) aims at improving visual modelling; it tries to (a) maximise diagrammatic cognitive effectiveness, (b) increase visual expressivity, and (c) level of rigour and formality. It is an alternative to UML that does largely pictorially what is traditionally done textually. The paper presents the results of a controlled experiment carried out four times in different academic settings and involving 43 participants, which compares VCL against UML and OCL and whose goal is to provide insight on benefits and limitations of visual modelling. The paper's hypotheses are evaluated using a crossover design with the following tasks: (i) modelling of state space, invariants and operations, (ii) comprehension of modelled problem, (iii) detection of model defects and (iv) comprehension of a given model. Although visual approaches have been used and advocated for decades, this is the first empirical investigation looking into the effects of graphical expression of invariants and operations on modelling and model usage tasks. Results suggest VCL benefits in defect detection, model comprehension, and modelling of operations, providing some empirical evidence on the benefits of graphical software design

On Formalizing UML and OCL Features and Their Employment to Runtime Verification

Model-driven development (MDD) has been identified as a promising approach for developing software. By using abstract models of a system and by generating parts of the system out of these models, one tries to improve the efficiency of the overall development process and the quality of the resulting software. In the context of MDD the Unified Modeling Language (UML) and its related textual Object Constraint Language (OCL) have gained a high recognition. To be able to generate systems of high quality and to allow for interoperability between modeling tools, a well-defined semantics for these languages is required. This thesis summarizes published work in this context that employs an endogenous metamodeling approach to define the semantics of newer elements of the UML. While the covered elements are exhaustively used to define relations between elements of the metamodel of the UML, the UML specification leaves out a precise definition of their semantics. Our proposed approach uses models, not only to define the abstract syntax, but also to define the semantics of UML. By using UML and OCL for this, existing modeling tools can be used to validate the definition. The second part of this thesis covers work on the usage of UML and OCL models for runtime verification. It is shown how models can still be used at the end of a software development process, i. e., after an implementation has manually been added to generated parts, even though they are not used as central parts of the development process. This work also influenced the integration of protocol state machines into a modeling tool, which lead to publications about the runtime semantics of state machines and the capabilities to declaratively specify behavior using state machines

An Agent-Based Model of the IL-1 Stimulated Nuclear Factor-kappa B Signalling Pathway

The transcription factor NF-κB is a biological component that is central to the regulation of genes involved in the innate immune system. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects of NF-κB within promotion of inflammatory diseases. We believe that computational modelling and simulation of the NF-κB signalling pathway will complement wet-lab experimental approaches, and will facilitate a more comprehensive understanding of this example of a complex biological system. In this study, we have developed an agent-based model of the IL-1 stimulated NF-κB signalling pathway, which has been calibrated to wet- lab data at the single-cell level. Through rigorous software engineering, which followed a principled approach to design and development by adherence to the CoSMoS process, we believe our model provides an abstracted view of the underlying real-world system, and can be used in a predictive capacity through in silico experimentation. A novel approach to domain modelling has been presented, which uses linear and multivariate statistical techniques to complement the Unified Modelling Language. Furthermore, in silico experimentation with the newly developed agent-based model, has confirmed the robust yet fragile nature of the signalling pathway. We have discovered that the pathway is robust to perturbations of cell membrane receptor component number, intermediate component number, and the temporal lag between cell membrane receptor activation and subsequent activation of IKK. Conversely however, in silico experimentation predicts that the pathway is sensitive to changes in the ratio of free IκBα to NF-κB, and fragile to basal dissociation of NF-κB-IκBα outside of a narrow range of probabilities