Thomas Moser

DODT: Increasing requirements formalism using domain ontologies for improved embedded systems development

In times of ever-growing system complexity and thus increasing possibilities for errors, high-quality requirements are crucial to prevent design errors in later project phases and to facilitate design verification and validation. To ensure and improve the consistency, completeness and correctness of requirements, formal languages have been introduced as an alternative to using natural language (NL) requirement descriptions. However, in many cases existing NL requirements must be taken into account. The formalization of those requirements by now is a primarily manual task, which therefore is both cumbersome and error-prone. We introduce the tool DODT that semi-automatically transforms NL requirements into semi-formal boilerplate requirements. The transformation builds upon a domain ontology (DO) containing knowledge of the problem domain and upon natural language processing techniques. The tool strongly reduced the required manual effort for the transformation. In addition the quality of the requirements was improved.

Semantic Integration of Software and Systems Engineering Environments

Software-intensive systems in business information technology (IT) and industrial automation have become increasingly complex due to the need for more flexible system reconfiguration and business and engineering processes. Systems and software-engineering projects depend on the cooperation of experts from heterogeneous engineering domains using tools that were not designed to cooperate seamlessly. Current semantic-engineering-environment integration is often ad hoc and fragile, thereby making the evolution of tools and the reuse of integration solutions across projects unnecessarily inefficient and risky. This paper describes the engineering-knowledge-base (EKB) framework for engineering-environment integration in multidisciplinary engineering projects. The EKB stores explicit engineering knowledge to support access to and management of engineering models across tools and disciplines by providing 1) data integration based on mappings between local and domain-level engineering concepts; 2) transformations between local engineering concepts; and 3) advanced applications built on these foundations, e.g., end-to-end analyses. As a result, experts from different organizations may use their well-known tools and data models and can access data from other tools in their syntax. The research results have been evaluated in the industrial-application domain of software-intensive production-automation systems. The evaluation results indicate an effort-reduction for reuse in new engineering projects and finding defects earlier in the engineering process.

Ontology-driven guidance for requirements elicitation

Requirements managers aim at keeping their sets of requirements well-defined, consistent and up to date throughout a projects life cycle. Semantic web technologies have found many valuable applications in the field of requirements engineering, with most of them focusing on requirements analysis. However the usability of results originating from such requirements analyses strongly depends on the quality of the original requirements, which often are defined using natural language expressions without meaningful structures. In this work we present the prototypic implementation of a semantic guidance system used to assist requirements engineers with capturing requirements using a semiformal representation. The semantic guidance system uses concepts, relations and axioms of a domain ontology to provide a list of suggestions the requirements engineer can build on to define requirements. The semantic guidance system is evaluated based on a domain ontology and a set of requirements from the aerospace domain. The evaluation results show that the semantic guidance system effectively supports requirements engineers in defining well-structured requirements.

A Domain Ontology Building Process for Guiding Requirements Elicitation

[Context and motivation] In Requirements Management, ontologies are used to reconcile gaps in the knowledge and common understanding among stakeholders during requirement elicitation, and therefore significantly improve the quality of the elicited requirements.[Question/problem] However, a precondition of state-of-the-art ontology approaches for requirements elicitation is an existing domain ontology. While this is not a trivial precondition, there are only a few reports on approaches to systematically and efficiently build domain ontologies, and these approaches are often highly biased towards their intended use. [Principal ideas/results] In this paper, we investigate an approach for building domain ontologies suitable for guiding requirements elicitation. We evaluate the feasibility of the approach based on a real-world industrial use case by analyzing natural language text from technical standards. [Contribution] A major outcome is that the proposed approach can help reduce the effort of building domain ontologies from the scratch.

Version management and conflict detection across heterogeneous engineering data models

Automation systems engineering projects depend on contributions from several engineering disciplines. These contributions consist of complex artifacts like mechanical, electrical, and software components and plans, which get updated concurrently. While there are version management features in the software tools for each individual engineering discipline, there is very little work on version management across semantically heterogeneous data models in engineering tools and projects. In this paper, we introduce the Engineering Database (EDB) concept, which provides foundations for version management and update conflict detection in engineering data models across tool boundaries. We evaluate the concept based on a real-world use case for signal engineering at a hydro power plant systems integrator. Major result is that the parsing of proprietary engineering tool data exports can be generalized and the mappings between engineering tools can be simplified.

Semantic tool interoperability for engineering manufacturing systems

Manufacturing systems engineering projects depend on contributions from several engineering disciplines. These contributions consist of complex artifacts like mechanical, electrical, and software components and plans. While the software tools are strong in supporting each individual engineering discipline, there is very little work on engineering processes automation across semantically heterogeneous engineering tool data models. In this paper, we adapt the Engineering Knowledge Base (EKB) concept, a semantic model, which extends the Global-as-View concept and explicitly models common engineering concepts and mappings using machine-understandable syntax, for the engineering of manufacturing systems. We evaluate the concept based on a real-world use case for data exchange between software tools involved in the engineering of a manufacturing system software simulator. Major result is that the EKB concept sufficiently supports the semantic interoperability of tools to enable the automation of engineering processes.

Engineering project management using the Engineering Cockpit: A collaboration platform for project managers and engineers

Automation Systems Engineering projects typically depend on contributions from several engineering disciplines. While available software tools are strong in supporting each individual engineering discipline, there is very little work on engineering process management and monitoring across multi-discipline engineering projects. In this paper, we present the Engineering Cockpit, a social-network-style collaboration platform for automation system engineering project managers and engineers, which provides a role-specific single entry point for project monitoring, collaboration, and management. We present a prototype implementation of the Engineering Cockpit and discuss its benefits and limitations based on the feedback of our industry partners. Major results are that the Engineering Cockpit increases the team-awareness of engineers and provides project-specific information across engineering discipline boundaries.

RISK ASSESSMENT IN MULTI-DISCIPLINARY (SOFTWARE+) ENGINEERING PROJECTS

Software systems in safety-critical industrial automation systems, such as power plants and steel mills, become increasingly large, complex, and distributed. For assessing risks, like low product quality and project cost and duration overruns, to trustworthy services provided by software as part of automation systems there are established risk analysis approaches based on data collection from project participants and data models. However, in multi-disciplinary engineering projects there are often semantic gaps between the software tools and data models of the participating engineering disciplines, e.g., mechanic, electrical, and software engineering. In this paper we discuss current limitations to risk assessment in (software+) engineering projects and introduce the SEMRISK approach for risk assessment in projects with semantically heterogeneous software tools and data models. The SEMRISK approach provides the knowledge engineering foundation to allow an end-to-end view for service-relevant data elements such as signals, by providing a project domain ontology and mappings to the tool data models of the involved engineering disciplines. We empirically evaluate the effectiveness and efficiency of the approach based on a real-world industrial use case from the safety-critical power plant domain. Major results are that the approach was effective and considerably more efficient than the current approach at the industry partner.

Integrating Production Automation Expert Knowledge Across Engineering Stakeholder Domains

The engineering of complex production automation systems involves experts from several backgrounds, such as mechanical, electrical, and software engineering. The production automation expert knowledge is embedded in their tools and data models, which are, unfortunately, insufficiently integrated across the expert disciplines, due to semantically heterogeneous data structures and terminologies. Traditional integration approaches to data integration using a common repository are limited as they require an agreement on a common data schema by all project stakeholders. In this paper we introduce the Engineering Knowledge Base (EKB), a semantic-web-based framework, which supports the efficient integration of information originating from different expert domains without a complete common data schema. We evaluate the proposed approach with data from real-world use cases from the production automation domain on data exchange between tools and model checking across tools. Major results are that the EKB framework supports stronger semantic mapping mechanisms than a common repository and is more efficient if data definitions evolve frequently.

SAMOA - A Semi-Automated Ontology Alignment Method for Systems Integration in Safety-Critical Environments

The integration of heterogeneous data sources with even heterogeneous semantic meanings poses a challenge for data and system integrators. Ontology Alignment (OA) tries to identify similarities between heterogeneous ontologies and to automatically create suitable mappings for transformation. However, the usage of standard OA approach for safety-critical domains needs further investigation.In this paper, we describe a semi-automated ontology alignment approach (SAMOA) well-suitable for integration scenarios of safety-critical applications. The major contribution of our approach is the modeling differentiation between individual system knowledge and generic domain-specific knowledge.We evaluate our approach by providing a typical use case example from the Air Traffic Management (ATM) domain. In addition we analyze to what extent the SAMOA approach can be supported by state-of-the-art OA approaches.