You can ask, how does Prof Codd knows that this is is the correct way to describe and implement data. Well this follow directly from set theory. Again, how do we know that set theory works - well here we are the level of axioms of the set algebra.
BTW, this Codd work was also "hacked" again by system R research which invented SQL.
The other part of UML is based on finite state machine, which originated by Turning, and later refined by the work of David Harel on state charts. Again, those are theoretical CS tools. However, the work of Harel on state charts, was originated from the work on the Israel first aircarft (to be cancelled later), where he needed to define the the missile system.
If you are interested on how those tool originated and later combined see the work of David Hay - "UML-Data-Modeling-Reconciliation"
Those tools are useful in scale. For example, system with 3000 different use cases. Or for example you have 200 - 500 classes, each is a state machine.
Again, since there is no software professional standard (not software engineering bar exam), you can or cannot use anything.
Will UML help me deliver software with changing requirements faster and more accurately? How does it compare to other options in my toolbox, like using a less verbose language that doesn't have real OO (which in my experience is often the right choice) and because of its conciseness is easier to make major changes to? How do I tie the UML object structure to my programming language, and why is it better than using a strongly- and richly-typed language in the first place? Why do I see many successful software projects that as far as I can tell don't use UML?
The whole idea is to separate the global decisions (which are hard to change) - e.g. architecture, what classes, what each class do, from the local one (e.g. which data structure to use). So you would use UML for the global decisions, and than make programming the classes almost mechanical.
Of course the customer do not see that. The customer see the user interface, and if the overall use cases are implemented correctly. Hence using those tool might help or not.
Lets take architecture for example, why do people use an architect ?. Why does architect needs diagrams? Will the owner care of the architect use diagrams?
I am asking what evidence there is that UML helps me deliver better products more efficiently. Are there successful software projects that do this complete code generation thing (and how do they compare to directly writing real code in more efficient programming languages with rich typesystems)? Are there development efforts where giving up and deciding that certain architectural elements are hard to change has been the right approach (in my experience it generally never is)? What are their names? Do they have experience reports? Are there numbers?
I am looking for, at the least, a specific name like "The Chrysler Comprehensive Compensation System took these approaches to development." (But hopefully not a massive and expensive failure like C3.)
Even if you find such and such project did X and got Y, the sample size is too small.
The issue with software is that each project is different, and hence we cannot generalise from any given project.
And this would be one of the reasons why UML is objectively a terrible idea for developing great software efficiently. (I assume it is ok for developing poor software at great cost)
In my mind, the crux of the entire exercise is trying to translate a complex murky real world problem domain into a mental model that can be expressed via a shared language describing properties, aspects, processes, concepts, etc.
The challenge is getting that translation done in a timely, secure, efficient, performant, maintainable manner.
Quantifying that translation into a comparable measure? That's where you'll find your in the same boat with many other project managers who make guestimates and hope that everything pans out in the end.
Trying to see it as a purely computational problem will confront you with that other evergreen: the P equals NP problem.
My point is that trying to find a hard model that answers your particular question pertaining to UML is very hard since there is no neat general foundational model here that allows you to compute hard, quantifiable predictions about the usefulness of a given tool.
I suppose it's like with so many trades. It depends on the experience of the expert to assess what strategy is best. It's okay to question that experience in hopes of avoiding all too human biases that cloud our view from what we are actually trying to solve. Even so, questioning any and all statements because you can't boil it down to hard data equally might threaten to narrows one's view.
If your experience tells you that UML works for you as a tool, then, by all means, stick to UML. Even when there is more then one approach to a solution. If you use pen and paper to quickly draw stuff, well, that's equally valid if that works for you. I just wouldn't dismiss an approach because its usefulness can't be measured up front. Reality is far too complex and sometimes there's simply no point in dwelling too much upon all of this in the first place.
Yes, that's the problem. The pixels spent here are based on hypothesis, not experiments. You think one thing, I think something else, and not a joule of effort is spent measuring.
https://en.m.wikipedia.org/wiki/Falsifiability
That's what we are debating here: is this claim that use of UML is always markedly better when modelling any problem domain is true as observed through a sufficient number of data points?
I'm poking holes in that hypothesis because there's always n + 1 conceivable use case where the opposite is true. And I'm basing myself on set theory and computational algebra.
The crux is 'sufficient' because all use cases that could be moddeled with UML being equal, prove to me that there is a finite amount of use cases. Hard answer: you can't.
Moreover, there is no way you can prove to me that each and any use case is solvable with UML in an efficient way without actually solving said use case.
Check Hilbert's Grand Hotel paradox. Perfectly applicable to this question. https://en.m.wikipedia.org/wiki/Hilbert%27s_paradox_of_the_G...
And it's conceivable that the sun will not rise tomorrow, or that the law of gravity will pass an inflection point and reverse itself. Also based on "set theory and computational algebra", and the impossibly of testing each possibly.
What's your plan for tomorrow, and does it depend on gravity?
My point here is that those tools are theoretical CS tools based on the underlying nature of computers - which are a discrete digital state machine.
Why do you even use high level languages, why not just program in assembler?
Right now some people have a theory that Uncle Bob is noise in the sea of architectural opinion, and we can't move forward because we don't have enough data. The call for more data is welcome by me.
You won't be getting much data on this because companies are secretive.
The way it's used is to ensure correctness of distributed systems. A classic example might be ensuring that a model for backing up customer data is unable to get into indeterminate state. It's important to get the model right. But the model isn't code - TLA is not a programming language, it's a tool for modeling state machines; yes, of course, one needs to also write software to deliver value.
Yes. (Or more precisely, it never was useful.)
Zero part of my business requirements involve anything about discrete digital state machines. If I could deliver business value more efficiently with pen and paper, I would. If I could deliver business value more efficiently with Excel or a shell one-liner, I would (and do). If I could deliver business value more efficiently by singing my harmony into the Music of the Ainur, I would. I write software because it's the tool we've found that is most efficient at delivering business value, not because the software itself has value.
So I use high-level languages because they are demonstrably more efficient. (And I frequently write sloppy code in Python or awk because it helps me answer business-relevant questions like "why is the site slow" quickly, without demanding I be rigorous about my software engineering in the process of answering the question.) don't use UML because I have yet to see evidence that it will help me deliver business value more effectively.
(I do also enjoy writing software as a practice, and I do that on weekends. That's the time to make code for code's sake.)
However, the comment above (which was not me) said "studies cited, case studies presented or concrete examples," not just studies. I was taking objection to the claim that merely coming out of academia counts as a "study" in this sense. I don't need an experimental study (though I would love to have one), a body of anecdotes of successful uses of a tool is sufficient.
I can name countless case studies, concrete examples, and anecdotes of people using the programming languages and software environments I use successfully. There is a large body of evidence that, say, Linux works fine, whether or not a paper says as much. (And, in particular, there is a sizable body of anecdotal evidence that OpenStack does not work fine, and I've been pushing my team to find a migration strategy out of OpenStack for this exact reason.)
Also, I think the burden of proof is on someone advocating for change. When I am delivering software successfully, and someone else comes in and says "You need to be taking this approach," I think it's fair for me to say "Why?", and that that's very different from staring blankly at bash and saying "Why?" and refusing to type anything.
And finally I would actually be delighted to have real metrics for my tools. I do want to know if, say, enforcing style checks via pre-commit hooks makes development velocity faster and avoids annoying developers! It certainly seems like it would but it sounds like an easy enough thing to test, and I wish we as an industry (and more specifically people in either academia or industry who see their work as advocating for better software development practices, not developing software) would spend the time to test it.
(And there are plenty of things that industry does respect that came from academic research in the sense of new ideas. Rust, stereotypically the language of the trend-prone, draws on everything from Ada to functional programming. But unlike those it actually and demonstrably meets the needs of general-purpose programming in industry.)