Published Sep 3, 2019

Episode 67: Roundtable on MDSD and PLE

Tom Stahl moderates a roundtable at the Model-Driven Development and Product Lines conference, diving into the intricacies of modeling terminology, domain-specific languages, and model-driven development, with insights from industry leaders at SAP and Pure Systems on the importance of standards, effective visualization tools, and overcoming communication barriers.
Episode Highlights
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Episode Highlights

  • Visualization

    Visualizing code structures is a crucial tool for bridging the gap between developers and executives. highlights how developers can continue coding while generating diagrams at the push of a button, satisfying executives' demands for visual representations without the need for cumbersome UML tools 1. adds that this approach is akin to "alibi modeling," where developers can produce visual models from existing code, making it easier to communicate complex structures 1. This method is not about reverse engineering but rather about providing a visual aid to understand and present code structures 2.

    Together problem that you mentioned that addressed two pain points at once, and they did it in an appropriate way, killing those two pains.

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    emphasizes that this visualization is about understanding existing structures, not creating new ones from scratch 1.

       

    Reverse Engineering

    Reverse engineering through visualization is essential for comprehending legacy systems. explains that the goal is not to automatically convert legacy code into UML models but to understand the functionalities and assets within these applications 3. This process involves standardizing meta models to express software structures, allowing tool vendors to exchange findings and insights 3. notes the academic challenges in program understanding and architectural recovery, emphasizing the need for practical tools in the industry 3.

    It's without any use, but it is useful to understand the applications. Useful to express what you understood in agreed models.

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    warns of the risk of overcomplicating models with excessive requirements, which can render them ineffective 3.

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