I agree it is dismissive, but disagree that it is hopelessly so. Much of the visual or "graphical" programming hype over the years comes from a lack of understanding what text is and how well it works. The core of the issue with visual programming, is that as soon as you scale to a non-trivial example, it has (almost) always fallen apart.
I actually really, really like the idea of non-textual interpretations of data and code, and use them when text is cumbersome. But they are difficult to do well. Jupyter is an excellent step in the right direction, and a reason it has taken off, I think. Albeit within the statistics subset of software development.
I think it is hopelessly dismissive to denigrate the efforts of software researchers over the past 50+ years and say that they didn't try to figure out better ways of representing ideas. There have been great minds trying to figure out a better way to describe computation. Math and textual programming languages are what they keep coming back to! However, I think we should definitely keep trying, but the basis of new efforts has to be an understanding what text (and math formula) gives us.
> The core of the issue with visual programming, is that as soon as you scale to a non-trivial example, it has (almost) always fallen apart.
people always say this, but it almost always come from people who haven't actually tried to do so or seriously programmed in a visual language before.
> comes from a lack of understanding what text is and how well it works.
i don't think that's the case at all. i am a proponent of both text-based and visual programming languages, hoping to understand hybrid approaches better. if anything, i know where text really doesn't work. for example, text-based languages are terrible at representing the dataflow paradigm and are often more complex than they need to be. visual languages are rather good at this. so we have the situation that text-based languages are terrible at dataflow and no one cares about visual languages, so the dataflow paradigm remains relatively unused, only showing up implicitly in actor-based systems and in minimal ways (simply as pipes) in functional programming languages.
> I think it is hopelessly dismissive to denigrate the efforts of software researchers over the past 50+ years and say that they didn't try to figure out better ways of representing ideas.
i don't think so at all because i really don't think they've tried to specifically understand visual paradigms. i haven't seen efforts to do so, but i would love to see examples if they exist. i have spoken to a rather well-known computer scientist at a conference, and when i mentioned my interest in visual programming, i was immediately cut off mid sentence with the exact phrase "i don't believe in it".
I actually really, really like the idea of non-textual interpretations of data and code, and use them when text is cumbersome. But they are difficult to do well. Jupyter is an excellent step in the right direction, and a reason it has taken off, I think. Albeit within the statistics subset of software development.
I think it is hopelessly dismissive to denigrate the efforts of software researchers over the past 50+ years and say that they didn't try to figure out better ways of representing ideas. There have been great minds trying to figure out a better way to describe computation. Math and textual programming languages are what they keep coming back to! However, I think we should definitely keep trying, but the basis of new efforts has to be an understanding what text (and math formula) gives us.