Model Engineering Solutions GmbH (MES) successfully brought the MES Model Examiner® (MXAM) and MES M-XRAY® (MXRAY) on the market as tools for static model analysis in compliance with ISO 26262. Up until now, these tools were based on two separate technical platforms. With the new MES Model Examiner® v.6.x - Functional Safety Solution, they will now be merged.
The New MXAM v.6 - The Integrated Platform for Static Model Analysis
Metrics or Guidelines?
MXAM is the first-choice in guideline checking tools when it comes to compliance with established modeling guidelines for model implementation. MXRAY focuses on model structure analysis and measures metrics to evaluate different aspects of the architectural design of models. In the latter, the boundaries between pass and fail results may be vague and/or may require the context of other metrics. Despite their different focuses, however, both metrics and guidelines are essential aspects of static model analysis and should always be considered together. For this reason, both tools, MXAM and MXRAY, will combine to become the new MXAM v.6.x.
Technical Advantages
The combination of both tools offers a variety of new advantages and functions for the most comprehensive static model analysis in compliance with ISO 26262 to date in one tool. First and foremost, of course, this includes the extension of the guideline pool in MXAM to include ISO 26262 architectural design principles with the quality metrics of MXRAY. These guidelines focus on the balance between model structure and complexity and are compatible with proven MXAM user features such as the interactive review support, annotations, and ignore lists. In particular, the review support offers a convenient way to document design decisions in compliance with ISO 26262. The MXAM Report Perspective has also been expanded to include additional views that offer extensive filtering and display options for complex metric dependencies. In this way, experienced MXRAY users will be familiar with the metric overview in MXAM, and those users who have no experience with the MXRAY tool will gain a simple introduction to model metrics. All previous functions of MXRAY can now be found in MXAM, from model metrics to quality metrics and clone detection. Thus the use of the new MXAM v.6.x - Functional Safety Solution provides the perfect basis for a complete static model analysis in compliance with ISO 26262.
MXRAY v.4.3 - The Long-term Supported Version (LTS) of MXRAY
For users who currently use MXRAY locally or automated as a stand-alone tool, MES will release a long-term supported version: MES M-XRAY v.4.3 (LTS). Support for bug fixing of the LTS is guaranteed for the next five years until April 30, 2025.
MES software support in conjunction with third-party software (i.e. MATLAB Simulink/Stateflow, dSPACE TargetLink, ETAS ASCET) is generally limited to the corresponding third-party software versions released before the release of the respective MES software version. All supported versions are listed in the release notes.
Expand Your Knowledge with tudoor academy
Webinar: Guidelines are a Modeler's Best Friend - Alongside best practices for modeling guidelines and model complexity, the webinar will cover the fundamental principles of static model analysis.
Training: MXAM in Action - Best Practices for Modeling Guidelines and Architectural Design Principles - This training class teaches you how to develop MISRA- and ISO 26262-compliant models by applying established modeling standards and best practices. The emphasis is on the effective integration of the MES Model Examiner (MXAM) into the development workflow.
In-House Training: MXAM Guideline Selection & Configuration - This consulting package is targeted at users of MES Model Examiner (MXAM) who intend on customizing their set of modeling guidelines to their specific needs.
What Model Metrics Have to Do With Software Quality
In this webinar, we will demonstrate which model metrics can significantly enhance model quality based on key design principles, offer strategies to aid interpretation, share best practices for applying these metrics, and show how these improvements lead to better overall model performance.
Related Topics
