When you give up the work of deciding what the expected inputs and outputs of the code/program is you are no longer in the drivers seat.
You don’t need to write tests for that, you need to write acceptance criteria.
That would be interesting. Of course, gherkin tends to just be transpiled into generated code that is customized for the particular test, so I'm not sure how AI can really abstract it away too much.
You need some of way of precisely telling AI what to do. As it turns out there is only that much you can do with text. Come to think of it, you can write a whole book about a scenery, and yet 100 people will imagine it quite differently. And still that actual photograph would be totally different compared to the imagination of all those 100 people.
As it turns out if you wish to describe something accurately enough, you have to write mathematical statements, in other words statements that reduce to true/false answers. We could skip to the end of the discussion here, and say you are better of either writing code directly or test cases.
This is just people revisiting logic programming all over again.
I think this is the detail you are not getting quite right. The truth of the matter is that you don't need precision to get acceptable results, at least in 100% of the cases. As everything in software engineering, there is indeed "good enough".
Also worth noting, LLMs allow anyone to improve upon "good enough".
> As it turns out if you wish to describe something accurately enough, you have to write mathematical statements, in other words statements that reduce to true/false answers.
Not really. Nothing prevents you to refer to high-level sets of requirements. For example, if you tell a LLM "enforce Google's style guide", you don't have to concern yourself with how many spaces are in a tab. LLMs have been migrating towards instruction files and prompt files for a while, too.
But if you want a near 100% automation, you need precise way to specify what you want, else there is no reliable way interpreting what you mean. And by that definition lots of regression/breakage has to be endured everytime a release is made.
> When you give up the work of deciding what the expected inputs and outputs of the code/program is you are no longer in the drivers seat.
You don’t need to personally write code that mechanically iterates over every possible state to remain in the driver’s seat. You need to describe the acceptance criteria.
Those does not involves writing state transitions. You are merely describing the acceptance criteria. Imperative is the norm because that's how computers works, but there are other abstractions that maps more to how people thinks. Or how the problem is already solved.
Acceptance criteria might be something like “the user can enter their email address”.
Tests might cover what happens when the user enters an email address, what happens when the user tries to enter the empty string, what happens when the user tries to enter a non-email address, what happens when the user tries to enter more than one email address…
In order to be in the driver’s seat, you only need to define the acceptance criteria. You don’t need to write all the tests.
That only defines one of the things the user can enter. Should they be allowed to enter their postal address? Maybe. Should they be allowed to enter their friend's email address? Maybe.
Your acceptance criteria is too shy of details.
There is no prescriptive manner in which to deliver the solution, unless it was built into the acceptance criteria.
You are not talking about the same thing as the parent.
You're describing the happy path of BDD-style testing frameworks.
What level do you think there is above "Given I'm logged in as a Regular User When I go to the front page Then I see the Profile button"?
I'm describing a scenario as implemented in a gherkin feature file. A feature is tracked by one or more scenarios.
https://cucumber.io/docs/gherkin/reference/
> Do you seriously think there is no higher level than given/when/thens?
You tell me which higher level you have in mind.
The problem is that tests are for the unhappy path just as much as the happy path, and unhappy paths tend to get particular and detailed, which means even in gherkin it can get cumbersome.
If AI is to handle production code, the unhappy paths need to at least be certain, even if repetitive.
I don't think that's how gherkin is used. Take for example Cucumber. Cucumber only uses it's feature files to specify which steps a test should execute, whereas steps are pretty vanilla JavaScript code.
In theory, nowadays all you need is a skeleton of your test project, including feature files specifying the scenarios you want to run, and prompt LLMs to fill in the steps required by your test scenarios.
You can also use a LLM to generate feature files, but if the goal is to specify requirements and have a test suite enforce them, implicitly the scenarios are the starting point.
Sir, those are called tests.
Acceptance criteria is a human-readable text that the person specifying the software has to write to fill-up a field in Scrum tools and not at all guide the work of the developers.
It's usually derived from the description by an algorithm (that the person writing it has to run on their mind), and any deviation from that algorithm should make the person edit the description instead to make the deviation go away.
You're not familiar with automated testing or BDD, are you?
> (...) to fill-up a field in Scrum tools (..)
It seems you are confusing test management software used to tracks manual tests with actual acceptance tests.
This sort of confusion would be ok 20 years ago, but it has since went the way of the dodo.
Isn't that logic programming/Prolog?
You basically write the sequence of conditions(i.e tests in our lingo) that have to be true, and the compiler(now AI) generates code for your.
Perhaps there has to be a relook on how Logic programming can be done in the modern era to make this more seamless.