A look at why model-based approaches found a home in systems engineering, even as they are cast aside in the software disciplines.

Model-Based Systems Engineering (MBSE) now feels fairly normal in places where system complexity, multiple disciplines, and lifecycle visibility are taken seriously.  This discipline uses SysML models to document, simulate, and formally validate systems; in some cases even generating the VHDL used to program FPGAs.

These model-centric concepts were available to software engineers years before they were mature for the systems side. Some came through the Shlaer-Mellor lineage, including later work on xtUML and Executable UML, some through Model-Driven Architecture (MDA), and some through formal methods.. Long before ‘S’ in MBSE meant “Systems”, I was working with a tool set where the same acronym was used to mean “Software”.  The software side has now fallen back to the more generic Model Driven Development (MDD), or sometimes Model-Driven Software Development (MDSD).  Yet even as the techniques took hold in the systems engineering community, they never really found solid footing among the software professionals. 

The most probable explanations aren’t that systems engineers were somehow more virtuous, or that software engineers don’t appreciate good ideas. It seems closer to say that the two professions were being pressed by different operating conditions, and so they developed different attitudes about discipline, abstraction, and up-front structure. 

Part of that difference, I suspect, is that systems engineering remained a more coherent professional culture. Its practitioners were expected to think across disciplines, interfaces, and lifecycle stages, which made shared abstractions feel like part of the job rather than an imposition on it. Software engineering was always broader and less unified. “Software engineer” can mean an embedded developer working close to hardware, an enterprise developer living in large business systems, or someone whose whole world is web frameworks and deployment pipelines. Those are not trivial variations inside one stable culture. They produce different habits of mind, and different levels of patience for methods that ask people to slow down, agree on structure, and maintain artifacts whose payoff may not arrive until much later.

It also seems relevant that systems engineering did not view agility only as a demand to strip away ceremony. The MBSE world appears to have spent a fair amount of effort trying to hybridize the two, speaking of Agile MBSE, middleweight processes, and even lean modeling rather than treating models themselves as waste. Software culture, at least in many of its more visible forms, was often more comfortable treating documentation, up-front structure, and intermediate representational layers as overhead unless the domain made them unavoidable.

Systems engineering already had habits of mind that made model-based discipline feel familiar. Those in the profession were accustomed to explicit interfaces, cross-disciplinary coordination, lifecycle coherence, and the recurring inconvenience that a problem in one part of a system rarely stays there. In that setting, the modeling abstraction was one of the few ways to keep the whole system visible without drowning in detail.

That seems important. If your work already assumes that electrical, mechanical, software, safety, human factors, and operational concerns are closely interwoven, then a model isn’t just an academic demand. It’s a more disciplined way to carry meaning across boundaries that already exist. Systems engineering was not being asked to discover structure. It already understood structure. The model-centered part was really an extension of a worldview it already had.

Software grew up under somewhat different conditions. Much of it, especially outside higher-assurance work, has been shaped by shorter feedback loops, local delivery pressure, local team autonomy, and environments where ambiguity may have a long life before anyone is forced to pay for it. Code can often be corrected after release. Behavior can often be observed in production. Design weaknesses can sometimes be tolerated for quite a while, provided the system keeps doing enough useful work to stay alive.

That changes the economics around discipline. Shared abstractions require agreement, and agreement takes time. Explicit models create obligations, and someone eventually has to maintain them. Up-front structure narrows room for improvisation, which is not always welcomed by organizations already rewarded for visible movement. So, in many software settings, model-based rigor had to be justified in an environment that had learned to live with a fair amount of ambiguity.

Professions don’t generally adopt methods simply because the methods are intellectually respectable. They adopt methods only when the benefits become obvious to them. If the cost of ambiguity is deferred, then ambiguity can remain socially affordable for a surprisingly long time. If the cost arrives early, and in a form that other people can see, then benefits of explicit structure become much more obvious.

That, perhaps, is part of why systems engineering was more receptive. It was already paying a visible price for incoherence. Interfaces that did not line up, assumptions that did not survive contact with neighboring disciplines, lifecycle decisions that could not be traced cleanly forward or backward; those were not abstract risks. They were chronic irritants, and sometimes worse. Under those conditions, a method that promised stronger design coherence had a fair chance of being recognized as useful.

Software had other complications, too.  A fair number of model-centered and formal approaches arrived with real friction attached. While there were some excellent tools, many, especially in the earlier generations, were quite clunky.  Integration with ordinary development workflows could be weak. Education was uneven. The literature could be persuasive at a conceptual level while leaving a practicing engineer with only a foggy idea of how any of this was supposed to survive an actual schedule, an actual team, and the sort of ordinary organizational untidiness that attends most real projects. Some methods were sound; some were cumbersome.

What might not be obvious to some is that, with respect to model-based systems engineering, and model-based software development, the technologies are not so far apart. What systems engineering now does with SysML and downstream implementation flows can be seen, at least in part, as an extension of capabilities software model-to-code approaches had already been exploring for years. The divergence seems to have had less to do with technical possibility than with where the surrounding culture, tooling, and incentives made the discipline sustainable. Some software engineers had bad experiences and concluded that model-based development was worthless. More likely, many experienced immature tools, weak adoption paths, or poor fit in the settings where they encountered it. There was also resistance directed at learning curves, adoption costs, and usability issues.  

Despite limited acceptance in the software community, the ideas did not disappear. They persisted in domains where the cost of being wrong was high enough to keep the underlying discipline from looking optional. Formal methods survived in selected high-assurance organizations. Executable modeling survived in narrower communities. Model-based engineering continued in places where cross-disciplinary visibility, mission assurance, safety obligations, or certification pressure made the deeper connections captured in a software model especially valuable.  It also survived in some companies that didn’t broadcast their successes. There are at least a few published accounts describing model compilers and accumulated application models as reusable IP and even as a source of competitive advantage.

Viewed in that way, the divergence between systems engineering and software is less mysterious. Systems engineering had an appetite for shared abstractions because the work kept forcing them into view. Software engineering often rewarded local success long before it rewarded whole-system comprehension. If a team could deliver features, patch defects, and keep moving, many structural weaknesses could remain deferred problems. Under those conditions, methods whose payoff arrived later were always going to face a harder reception, even when their long-term logic was sound enough.

Methods don’t change on merit alone. People cling to the familiar until the cost of not changing becomes onerous when compared with the perceived cost of making the change.

That may be the real dividing line. In software domains where local delivery is enough, model-driven development techniques will often seem too expensive to justify. But where the deeper structure of the system must remain visible across disciplines, stakeholders, and time, a model can become the vehicle for shared understanding and organizational memory, as well as a single source of truth for the software system.