Allen, J., "Managing Data and the Testing Process in the MBD Environment," SAE Technical Paper 2014-01-2149, 2014, doi:10.4271/2014-01-2149.
In the last few years, we have seen a tremendous increase in the rise in product complexity due to advances in technology and aircraft system functionality enhancement. The Model-based Design (MBD) process has helped manage the complexity of these systems while making product development faster by bringing more effective tools and methods to the entire process. Developing software using MBD has required extensive, sophisticated tool-chains that allow for efficient rapid controls prototyping, automatic code generation, and advanced validation and verification techniques using model-in-the-loop (MIL), software-in-the-loop (SIL), and hardware-in-the-loop (HIL) for both component testing and integration testing. However, the MBD process leads to generation of large volumes of data artifacts and work-products throughout the V-Cycle. The various components of these environments, from models to parameters to tests, can be inundating, and variants and versions of these artifacts lead to even larger amounts of data. These artifacts have traditionally been managed with Configuration Management systems and Product Lifecycle Management (PLM) tools, but the process of managing the links and information about this data (aka metadata) has been a difficult task for many companies. Many companies have limited or very poor integration between their PLM systems and their development tools for MBD. In order to effectively use the MBD tools in the development process, it is necessary to be able to manage this data and metadata in an efficient manner that relates directly to the engineering tools and methodsThis paper will discuss some of the major data artifacts and work products that are inherent in the MBD process. We will show the critical requirements to enable reuse of this data, in respect to both the actual MBD tools and the development and testing process leading to version control and variant management of these artifacts. A new Data Management environment has been built to support MBD systems given these requirements, and this system's approach to these issues will be shown. Examples for Test Management, Model Management, and Parameter Management will be discussed, along with the underlying needs to connect to Requirement Systems and provide process traceability. Further, we will discuss integration with standard PLM and Application Lifecycle Management (ALM) tools and processes, providing a useable MBD data-management solution to increase process efficiency and provide effective management of data.