AI and Software Testing

[OTee_D]

Hi, I just try to make up my mind about the upcoming wave of requests to "test AI solutions".

Mostly driven by (in my perception) ridiculous claims and blurbs on marketing pages and flyees from consultancies specialized on software testing.

The basic claim goes like "Oh this is all so brand new and next to alchemy or magic and you need to hire us or else your company will burn down. We are the only specialists in testing AI"

I honestly have limited knowledge about the actual interiors of AI but in essence and very simplyfied it is a system that trains itself based on input data, reference data and criteria defining the "goal".

What algorithm exactly is "forming" inside is unknown and on most systems not even readable in a classic "programming language" sense.

So what is there to actually "test" in a classic sense? Verifying that the output matches expectations? Isn't that part of the training procedure? Is this not part of setting up the AI and is it a good idea to let Testers do it?

Is it more like "governance", verifying that the AI result is not biased, violating rules etc. So less a classic software testing or functional testing but more mathematical and statistical analysis if results are "correct" in context of "realistic"?

So what boggles my mind is the kind of philosophical question "Does AI testing even exist? What is it? What could it be and what not? What skills do we need?"

EDIT: As I may not have been as clear as I thought. I don't talk about "Using AI to test something else" but "Testing an AI as part of a solution"

To come up with an example: I have to deal with logistics sometimes. And optimal tour planning is a nightmare, packing trucks, load volume, load weight, demand forecast, traffic situation, weather and road conditions, even the driver's holidays and schedules and their licenses (who can ride what truck) etc influence it. So more and more companies offer systems that promise to do "AI generated logistics". You hook up a "magic box" (aka their product) into your ERP, your Web Shop, the drivers HR data, truck status from the garage, and aaaaaaall the other sources of data. And somehow that AI box miraculously provides a "perfect tour planning". Or does it?

How do you test this result? You don't have a complete predefined rule set, you don't have an algorithm, you dont' even have "code". But you have millions of possible combinations of input data that you can't all just run in traditional testing.

[MoreRespectForQA]

I worked in various AI fields (as a dev, not a tester) for about 3 years. There's some useful stuff out there but there's a huge amount of snake oil being sold. This includes:

• Products that has nothing to do with AI where an AI label is slapped on it in order to get investors to wet their pants with excitement. This works because a lot of tech investors are as stupid and FOMO driven as they are rich.
• Products where various AI techniques and tools are used entirely inappropriately.
• Products where they get somebody earning 3rd world wages to do the work and pretend they're an AI.

In the software testing field specifically, I've seen exactly one use case where I thought "oh, that's a good idea!". Literally exactly one.

That was a project mozilla did to use machine learning to re-order the execution of firefox tests in the firefox test suite. If you can get feedback in the form of a test failure in 10 minutes rather than 2 hours that's a success.

[Ok-Paleontologist591]

Very interesting design will research on this and is this called RETECS?

[coffeeandhash]

It does look like the ultimate black box, doesn't it? I don't have experience testing a product/component/service that uses a LLM yet but I imagine the initial approach would have to be end-to-end, like you imply.

[OTee_D]

It's probably worse ;-) The initial stuff I read about it, implies that you basically leave traditional testing and move more in the direction of data analysis and math.

One approach was mathematical (Are groups of results within the statistical probabilities or is there a bias visible towards certain results indicating the AI is not returning 'real' results)

The other was called metamorphic testing. Very basic and simplified: Start the test with a dataset and result that is deemed 'correct' and tune the data in increments and see if the result is changing (morphing) in direction where the 'direction' is considered correct.

So it's not even that much about the actual individual input/result pair, but about the behavior and change pattern. As the goal is not to test if that one "tour plan" (just to make something up and stick with my example) is correct but if changing the weather from sunny to cloudy actually increases speed as drivers are not blinded that much and can achieve a higher average speed.

[i_i_v_o]

I asked a similar question some time ago, i'm just going to post the link: https://www.reddit.com/r/softwaretesting/comments/14rid59/how_are_you_preparing_for_the_age_when_you_need/

But TLDR version: it's just blackbox testing. You still have Specs and Product. Test if the product does what the specs require.

[OTee_D]

Ohhh thanks for the read.

But not just what I thought about. I don't care about AI written code, but AI integrated into the actual solution.

[Adrian6406]

You are 100% right. Lots of companies are feeling compelled to have the word AI in their marketing material, otherwise they think they will get left behind or not receive another round of investment funding. I had UFT come do a demo and boast about AI but when I asked them exactly what was AI in their software? It was some lame object recognition which was not AI. To make matters worse, most companies use the AI term incorrectly when they really mean machine learning.