Our free system model validation tool guides modeling consistency to reduce errors, aid analyses, and improve quality.

SAIC provides the Digital Engineering Validation Tool to the worldwide systems modeling and engineering community. Intended for system modelers, engineers, and users, this tool improves the quality and functionality of your system models using state-of-the-art validation techniques we developed. We hope that community use of this validation tool will stimulate adoption and discussion of industry best practices.

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Validation Tool

OUR TOOL'S SET OF VALIDATION RULES AND CUSTOMIZATIONS ENSURES MODELING RIGOR

Intended to guide model development where multiple options are available, the tool ensures that a team of modelers always makes the same choice, creating model consistency. SAIC continues to mature its popular Digital Engineering Validation Tool, which consists of:

• SAIC DE Profile (validation rules and customizations)
  • Validation Rules (both language and style) for Dassault Systemes/No Magic's MagicDraw and Cameo Enterprise Architecture
  • Customizations (including methods to connect deeply-nested ports, manage classification and data rights, and conduct failure analysis)
• Model-Based Style Guide
• Example System Model (based upon the Ranger lunar probe)
• Explanatory Videos

Immediate Benefits. Use of the validation rules in our tool should have an immediate and measurable impact on model quality. Style guides and language semantics are automatically enforced. This enables reviews of system content using analysis tools rather than human reviews, reducing review time from weeks to minutes for large system models. The customizations we provide also allow synchronization between the parts that make a system architecture (structure) and the functions they perform (behavior).

In December 2019, SAIC released v1.0 of its Digital Engineering Validation Tool. This represented the largest public release of automated validation rules for SysML models to date, and since then we have updated the tool to expand its scope and capabilities. Notable additions include:

• Growth from 126 to 221 rules
• Support for IBM’s Rhapsody modeling tool
• An example model based on the Ranger lunar probe (available in both Cameo and Rhapsody formats)
• A model-based style guide explaining the rules and modeling approach
• A quickstart model template
• Proof-of-concept profiles for classification markings and failure analysis
• How-to videos

V2.0 RELEASE OF THE VALIDATION TOOL.The rules and modeling style have been expanded in the v2.0 release to provide a rigorous method for federating disparate models. This enables a system-of-systems model to be created that automatically verifies interfaces between peer system models and identifies mismatches. Five new videos are included in this release, detailing both the philosophy and the mechanics of our federation approach.

This new approach was used to analyze the Mars Octet, a series of eight Mars mission models developed by students at the University of Detroit Mercy. Twenty-three interface connections were analyzed:

• 43% did not have fully consistent item flows/conveyed information
• 1 interface was not defined correctly (type mismatch)
• 101 of 548 total conveyed signals were incorrect/missing (18.4%)
• 6 interfaces were responsible for 78 of these errors (likely a maturity issue)
• 14 interfaces had 3 or fewer errors (accounting for the remaining 23 errors)

SAIC believes that this demonstrates the utility of our model federation approach, as well as the need for continued and expanded adoption of automated validation of descriptive and/or executable models. Detecting and remediating errors in descriptive architectures eliminates waste (by ensuring development efforts are supported by complete, consistent and correct architectural information) and reduces risk (by decreasing the number of avoidable errors in integration and test).

The release also includes a proof of concept that illustrates how our one-page process can be linked to other planning information to help identify model content that is needed to support program-specific goals. Traceability within the style guide can then be leveraged to thoughtfully tailor the process and related validation rules for a given digital engineering effort.

We wish to report that Steven Graves found an Easter egg we included in the Ranger example model (and we sent him a small token of appreciation for finding it). The Easter egg was a set of prefixed units to measure awesomeness; we will leave them in the model as an example.

Comments? We welcome feedback about our approach (and are particularly interested in false positives, negatives, or corner cases related to the rule set); please reach out to us at DigitalEngineering@saic.com.

Previous Releases: View the previous release notes here.