Abstract-As Model Based Systems Engineering (MBSE) practices gain adoption, various approaches have been developed in order to simplify and automate the process of generating documents from models. Essentially, all of these techniques can be unified around the concept of producing different views of the model according to the needs of the intended audience. In this paper, we will describe a technique developed at JPL of applying SysML Viewpoints and Views to generate documents and reports. An architecture of model-based view and document generation will be presented, and the necessary extensions to SysML with associated rationale will be explained. A survey of examples will highlight a variety of views that can be generated, and will provide some insight into how collaboration and integration is enabled. We will also describe the basic architecture for the enterprise applications that support this approach. and formal ontology expressed in the terminology and lexicon of each engineering domain. MBSE promises to alleviate the difficulty systems engineers face in communicating across engineering disciplines primarily in terms of completeness and consistency. By describing these systems in a formal way using domain specific terms, models can be checked for completeness and consistency. These models can also be analyzed to answer questions about the system such as input to simulations or other engineering analysis.At the core of realizing these benefits is effective commu- As MBSE practice has begun to move into the mainstream, several homegrown approaches have been developed around the use of the DocBook standard for publishing [6]. In general, these approaches involve the use of a SysML profile for DocBook to produce a model of a document. The document model is then linked to other SysML models and diagrams to produce the document.These approaches are effective at generating the basic structure of the document with injected model information. However, they lack the semantics and patterns to describe how the model is projected into a document structure. Each existing implementation has attempted different ways to support this, but none of these applications provides a comprehensive set of capability. They also lack a more fundamental concept and foundational support for describing how to extract information from the model in such a way so that analysis and editing of that information can be integrated with external applications. MGSS

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Most complex systems nowadays heavily rely on software, and spacecraft and satellite systems are no exception. Moreover as systems capabilities increase, the corresponding software required to integrate and address system tasks becomes more complex. Hence, in order to guarantee a system's success, testing of the software becomes imperative. Traditionally exhaustive testing of all possible behaviors was conducted. However, given the increased complexity and number of interacting behaviors of current systems, the time required for such thorough testing is prohibitive. As a result many have adopted random testing techniques to achieve sufficient coverage of the test space within a reasonable amount of time. In this paper we propose the use of genetic algorithms (GA) to greatly reduce the number of tests performed, while still maintaining the same level of confidence as current random testing approaches. We present a GA specifically tailored for the systems testing domain. In order to validate our algorithm we used the results from the Dawn test campaign. Preliminary results seem very encouraging, showing that our approach, when searching the worst test cases, outperforms random search , limiting the search to a mere 6 % of the full search domain. 1 2

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Without rigorous system verification and validation (SVV), flight systems have no assurances that they will actually accomplish their objectives (e.g., the right system was built) or that the system was built to specification (e.g., the system was built correctly). As system complexity grows, exhaustive SVV becomes time and cost prohibitive as the number of interactions explodes in an exponential or even combinatorial fashion. Consequently, JPL and others have resorted to selecting test cases by hand based on engineering judgment or stochastic methods such as Monte Carlo methods. These two approaches are at opposite ends of the search spectrum, in which one is narrow and focused and the other is broad and shallow. This paper describes a novel approach to test case selection through the use of genetic algorithms (GAs), a type of heuristic search technique based on Darwinian evolution that effectively bridges the search for test cases between broad and narrow spectrums. More specifically, this paper describes the Nemesis framework for automated test case generation, execution, and analysis using GAs. Results are presented for the Dawn Mission flight testbed.