> There are two main schools of thought in software development about how to build really big, complicated stuff.
> The most prevalent one, these days, is that you gradually evolve the complexity over time. You start small and keep adding to it.
> The other school is that you lay out a huge specification that would fully work through all of the complexity in advance, then build it.
I think AI will drive an interesting shift in how people build software. We'll see a move toward creating and iterating on specifications rather than implementations themselves.
In a sense, a specification is the most compact definition of your software possible. The knowledge density per "line" is much higher than in any programming language. This makes specifications easier to read, reason about, and iterate on—whether with AI or with peers.
I can imagine open source projects that will revolve entirely around specifications, not implementations. These specs could be discussed, with people contributing thoughts instead of pull requests. The more articulated the idea, the higher its chance of being "merged" into the working specification. For maintainers, reviewing "idea merge requests" and discussing them with AI assistants before updating the spec would be easier than reviewing code.
Specifications could be versioned just like software implementations, with running versions and stable releases. They could include addendums listing platform-specific caveats or library recommendations. With a good spec, developers could build their own tools in any language. One would be able to get a new version of the spec, diff it with the current one and ask AI to implement the difference or discuss what is needed for you personally and what is not. Similarly, It would be easier to "patch" the specification with your own requirements than to modify ready-made software.
“A complex system that works is invariably found to have evolved from a simple system that worked. The inverse proposition also appears to be true: A complex system designed from scratch never works and cannot be made to work. You have to start over, beginning with a working simple system.”
Gall’s Law
But this is about the first systems? I tend to tell people, the fourth try usually sticks.
The first is too ambitious and ends in an unmaintainable pile around a good core idea.
The second tries to "get everything right" and suffers second system syndrome.
The third gets it right but now for a bunch of central business needs. You learned after all. It is good exactly because it does not try to get _everything_ right like the second did.
The fourth patches up some more features to scoop up B and C prios and calls it a day.
Sometimes, often in BigCorp:
Creators move on and it will slowly deteriorate from being maintenaned...
The Evolution method outlined also seems born from the Continuous Delivery paradigm that was required for subscription business models. I would argue Engineering is the superior approach as the Lean/Agile methods of production were born from physical engineering projects whose end result was complete. Evolution seems to be even more chaotic because an improper paradigm of 'dev ops' was used instead of organically emerged as one would expect with an evolving method.
Ai assistance would seem to favor the engineering approach as the friction of teams and personalities is reduced in favor of quick feasibility testing and complete planning.
Software cannot be built like skyscrapers because the sponsors know about the malleability of the medium and treat it like a lump of clay that by adding water can be shaped to something else.
You're mixing up design and manufacturing. A skyscraper is first completely designed (on paper, cad systems, prototypes), before it is manufactured. In software engineering, coding is often more a design phase than a manufacturing phase.
Designers need malleability, that is why they all want digital design systems.
But software is in fact not very malleable at all. It's true the medium supports change, it's just a bunch of bits, but change is actually hard and expensive, perhaps more than other mediums.
A major factor supporting evolution over big up-front design is the drift in system requirements over time. Even on large military like projects, apparently there's "discovery"--and the more years that pass, the more requirements change.
> There are two main schools of thought in software development about how to build really big, complicated stuff.
> The most prevalent one, these days, is that you gradually evolve the complexity over time. You start small and keep adding to it.
> The other school is that you lay out a huge specification that would fully work through all of the complexity in advance, then build it.
I think AI will drive an interesting shift in how people build software. We'll see a move toward creating and iterating on specifications rather than implementations themselves.
In a sense, a specification is the most compact definition of your software possible. The knowledge density per "line" is much higher than in any programming language. This makes specifications easier to read, reason about, and iterate on—whether with AI or with peers.
I can imagine open source projects that will revolve entirely around specifications, not implementations. These specs could be discussed, with people contributing thoughts instead of pull requests. The more articulated the idea, the higher its chance of being "merged" into the working specification. For maintainers, reviewing "idea merge requests" and discussing them with AI assistants before updating the spec would be easier than reviewing code.
Specifications could be versioned just like software implementations, with running versions and stable releases. They could include addendums listing platform-specific caveats or library recommendations. With a good spec, developers could build their own tools in any language. One would be able to get a new version of the spec, diff it with the current one and ask AI to implement the difference or discuss what is needed for you personally and what is not. Similarly, It would be easier to "patch" the specification with your own requirements than to modify ready-made software.
Interesting times.
“A complex system that works is invariably found to have evolved from a simple system that worked. The inverse proposition also appears to be true: A complex system designed from scratch never works and cannot be made to work. You have to start over, beginning with a working simple system.” Gall’s Law
Ah, the Second System Effect, and the lesson learned from it.
But this is about the first systems? I tend to tell people, the fourth try usually sticks.
The first is too ambitious and ends in an unmaintainable pile around a good core idea.
The second tries to "get everything right" and suffers second system syndrome.
The third gets it right but now for a bunch of central business needs. You learned after all. It is good exactly because it does not try to get _everything_ right like the second did.
The fourth patches up some more features to scoop up B and C prios and calls it a day.
Sometimes, often in BigCorp: Creators move on and it will slowly deteriorate from being maintenaned...
So true.
The Evolution method outlined also seems born from the Continuous Delivery paradigm that was required for subscription business models. I would argue Engineering is the superior approach as the Lean/Agile methods of production were born from physical engineering projects whose end result was complete. Evolution seems to be even more chaotic because an improper paradigm of 'dev ops' was used instead of organically emerged as one would expect with an evolving method.
Ai assistance would seem to favor the engineering approach as the friction of teams and personalities is reduced in favor of quick feasibility testing and complete planning.
Software cannot be built like skyscrapers because the sponsors know about the malleability of the medium and treat it like a lump of clay that by adding water can be shaped to something else.
You're mixing up design and manufacturing. A skyscraper is first completely designed (on paper, cad systems, prototypes), before it is manufactured. In software engineering, coding is often more a design phase than a manufacturing phase.
Designers need malleability, that is why they all want digital design systems.
But software is in fact not very malleable at all. It's true the medium supports change, it's just a bunch of bits, but change is actually hard and expensive, perhaps more than other mediums.
With LLMs it's becoming very malleable.
A major factor supporting evolution over big up-front design is the drift in system requirements over time. Even on large military like projects, apparently there's "discovery"--and the more years that pass, the more requirements change.
Even if the requirements are indeed fixed your understanding of the problem domain evolves.