Srdjan Zivkovic

A Modelling Method for Consistent Physical Devices Management: An ADOxx Case Study

In the domain of network management and operation support systems (OSS), maintainance of thousands of physical network devices is a complex, error-prone and time-consuming task. Consistent device configuration and error identification among plethora of network device types is impossible without tool support. Domain-specific modelling (DSM) methods promise to deal with system complexity by raising the level of abstraction to models. In this paper, we report about conceptualization of a DSM method for physical devices management, that is implemented based on the ADOxx metamodelling platform. The method introduces a hybrid modelling approach. A dedicated DSML is used to model the structure of physical devices, whereas the ontology language OWL2 is used to specify configuration-related constraints. The work resulted in a novel semantic modelling tool prototype that leverage ontology reasoning technology to enhance the modelling.

On Developing Hybrid Modeling Methods using Metamodeling Platforms: A Case of Physical Devices DSML Based on ADOxx

It has been acknowledged that model-based approaches and domain-specific modeling DSM languages, methods and tools are beneficial for the engineering of increasingly complex systems and software. Instead of general-purpose one-size-fits-all modeling languages, DSM methods facilitate model-based analysis and design of complex systems by providing modeling concepts tailored to the specific problem domain. Furthermore, hybrid DSM methods combine single DSM methods into integrated modeling methods, to allow for multi-perspective modeling. Metamodeling platforms provide flexible means for design and implementation of such hybrid modeling methods and appropriate domain-specific modeling tools. In this paper, we report on the conceptualization of a hybrid DSM method in the domain of network physical devices management, and its implementation based on the ADOxx metamodeling platform. The method introduces a hybrid modeling approach. A dedicated DSM language DSML is used to model the structure of physical devices and their configurations, whereas the formal language for knowledge representation OWL2 is used to specify configuration-related constraints. The outcome of the work is a hybrid, semantic technology-enabled DSM tool that allows for efficient and consistency-preserving model-based configuration of network equipment.

Ontology-Guided Software Engineering in the MOST Workbench

This chapter reports about the software process guidance in ontology-driven software development (ODSD), one of the core ontology-enabled services of the ODSD environments. Ontology-driven software process guidance amounts to a significant step forward in software engineering in general (cf. Fig. 1.1 on p. 3). Its role is to guide developers through a complex software development process by providing information about the consistency of artefacts and about the tasks to be accomplished to reach a particular development goal.

Ontology Reasoning for Process Models

Processes in software development generally have two facets. They can be model objects, as described in Sect. 4.2, and also workflows, as described in Sect. 4.3. In this chapter, we analyse typical problems in process modelling and develop ontology reasoning technologies to address them in the ODSD infrastructure. We show how different ontological representation of process models can be constructed for different purposes and how reasoning can be applied to guarantee the consistency of models.

A Platform for ODSD: The MOST Workbench

In this chapter, we discuss how scalable reasoning technologies (cf. Chap. 5) can be transparently integrated into the existing model-driven software development (MDSD) environments and how MDSD environments leverage reasoning technology, in order to provide scalable ontology-based MDSD services. The successful realisation of ontology-driven software development (ODSD) vision requires a tight coupling of the introduced ontology reasoning services with existing MDSD technology. The challenge of building ontology-enabled MDSD environments is raised by the technological clash between conventional MDSD technology and reasoning technology.

Case Studies for Marrying Ontology and Software Technologies

In this chapter, we conclude Part I with several industrial case studies for motivating consistency-preserving software development. Many of these case studies will be revisited in later chapters, in particular Chaps. 9 and 10. Many of the solutions are based on the scalable reasoning technologies to be introduced in Chap. 5. The rest of this chapter is organised as follows. Section. 4.1 shows which problems companies meet when they want to specify correct and consistent domain models of telecommunication device configurations. Another case study (Sect. 4.2) treats consistency preservation for behavioural models (process models). In business process refinement, the more concrete, refined processes have to conform to the abstract business processes the consultant specified. Showing this form of consistency of refinement is not easy for the process architect, as it turns out. Section 4.3 presents the problem of consistency of product lines, their correct modelling of their variant spaces and the consistent selection of their variants.

Guidance in Business Process Modelling

This chapter shows how process modellers can be supported by guidance. If a telecommunication provider introduces a value-added service, this might involve the establishment of new business processes, whose specification is not trivial. A guidance engine can help a process engineer develop a new business process by stepwise refining, i.e. creating a more concrete version of the process from an abstract version. The guidance engine identifies inconsistencies and proposes possible refinement steps. The topics covered in this chapter range from theoretical foundations of business process refinement over the formalisation of refinement problems in ontologies to implementation issues. The presented solutions were developed in the MOST project.