Sebastian J. I. Herzig

A Conceptual Framework for Consistency Management in Model-Based Systems Engineering

Developing complex engineering systems requires the consolidation of models from a variety of domains such as economics, mechanics and software engineering. These models are typically created using differing formalisms and by stakeholders that have varying views on the same problem statement. The challenging question is: what is needed to make sure that all of these different models remain consistent during the design process? A review of the related literature reveals that this is still an open challenge and has not yet been investigated at a fundamental level within the context of Model-Based Systems Engineering (MBSE). Therefore, this paper specifically focuses on examining the fundamentals of consistency management. We show that some inconsistencies cannot be detected and come to the conclusion that it is impossible to say whether or not a system is fully consistent. In this paper, we first introduce a mathematical foundation to define consistency in a formal manner. A decision-based approach to design is then studied and applied to the development of a real-world example. The research reveals several distinct types of inconsistencies that can occur during the design and development of a system. We show that these inconsistencies can be further classified into two groups: internal and external consistency. From these insights, the ontology of inconsistencies is constructed. Finally, requirements for possible tool support and methods to identify and manage specific types of consistency issues are proposed.Copyright

An Approach to Identifying Inconsistencies in Model-based Systems Engineering

Abstract A typical way of managing the inherent complexity of contemporary technical systems is to study them from different viewpoints. Such viewpoints are defined by a variety of factors, including the concerns of interest, level of abstraction, observers and context. Views conforming to these viewpoints are typically highly interrelated since the concerns addressed in the different viewpoints overlap semantically. Such overlaps can lead to inconsistencies. The challenge is to identify and resolve – that is, manage – such inconsistencies. This paper introduces an approach to identifying inconsistencies within the context of Model- Based Systems Engineering (MBSE). In current practice, inconsistencies are typically only discovered after long time intervals, e.g., during reviews. This can result in costly rework or even mission failure. Therefore, actively checking for inconsistencies, and doing so in a continuous fashion, can be valuable. We investigate the hypothesis that all models can be represented by graphs and that inconsistencies can be identified by means of pattern matching. We show that this process is equivalent to inferring inconsistencies by means of deductive reasoning. Finally, we present the results of a proof-of-concept implementation.

A comparison of inconsistency management approaches using a mechatronic manufacturing system design case study

Designing and developing complex mechatronic systems requires the consolidation of models from a variety of domains. These models are created by different stakeholders using a variety of formalisms for the purpose of addressing specific concerns, and are used for representing different views on the same system. While it is considered good practice to separate concerns as much as possible, a complete separation is impossible. The resulting model overlap opens the possibility of inconsistencies being introduced - that is, disagreements between views. Numerous approaches to identify and resolve such inconsistencies have been introduced in the software and systems engineering literature. However, in mechatronic design practice, these have gained little acceptance. The goal of this paper is two-fold: to investigate why their wide spread use is not common, and to assess which of the approaches is the most promising for multi-disciplinary systems design. To aid in the investigation, a change scenario is analyzed using the models of a mechatronic manufacturing system as a case study.

Integrating analytical models with descriptive system models: implementation of the OMG SyML standard for the tool-specific case of MapleSim and MagicDraw

Abstract The Jet Propulsion Laboratory (JPL) is investing heavily in the development of an infrastructure for building system models using the Systems Modeling Language (SysML). An essential component is a transformation apparatus that permits diverse models to be integrated independently of their nature (e.g. declarative, analytical and statistical). This paper presents one useful case: the integration of analytical models expressed using the Modelica language. Modelica is an open standard, declarative, multi-domain modeling language that allows for complex dynamic systems to be modeled. Maplesofts MapleSim is one software tool that supports the Modelica language. The tool-neutral specification for the transformation between the languages Modelica and SysML is defined in the SysML-Modelica transformation specification (SyML) standard published by the Object Management Group (OMG). As part of the development efforts, said specification has been implemented using the Query-View- Transformation Operational (QVTO) language. During the process, several critical changes to the current SyML standard were proposed. Furthermore, a number of current limitations related to MapleSim were identified. Despite these issues, a proof-of- concept transformation was successfully implemented. In conclusion, the integration of complex simulation models conforming to the Modelica language with SysML-based system models has shown great promise and is a highly useful tool to support the decision making process in design.

Analyzing semantic relationships between multiformalism models for inconsistency management

The involvement of multiple stakeholders in the design of complex engineered system presents many challenges. One of the challenge is that the overlapping stakeholder concerns leads to semantic relationships appearing between models. From an inconsistency management perspective, it is critical to investigate how these relationships appear and what are their types. Based on a decision-theoretic foundation, this paper investigates the types of semantic relationships between multiformalism models. We argue that the semantic relationships can be formally captured for two cases - between model versions, and between models from a given instant. Further, we argue that semantic overlap for these two cases can be described through three relationships: equivalence, refinement and abstraction. These relationships can lead to the three types of inconsistencies: inconsistent constraints, inconsistent predictions and inconsistencies between specification and analysis. The paper presents a set of generic rules which can be used to detect these inconsistencies.