[adontz]

A little story from personal experience.

Most people think of feature flags as boolean on/off switches, maybe per user on/off switches.

If one is testing shades of colors for a "But Now!" button that may be OK. Regarding more complex tests my experience is that there are not a lot of users who tolerate experiments. Our solution was to represent feature flags as thresholds. We assigned a decimal number [0.0, 1.0) to each user (we called it courage) and a decimal number [0.0, 1.0] to a feature flags (we called it threshold). That way not only we enabled more experimental features for most experiment tolerant users, but these were the same users, so we could observe interaction between experimental features too. Also deploying a feature was as simple as rising it's threshold up to 1.0. User courage was 0.95 initially and could be updated manually. We tried to regenerate it daily based on surveys, but without much success.

[withinboredom]

Interesting. At one place I worked, employees were excluded from experiments (they had to enable the flag personally to see them) by default. At one point, we had so many experiments that literally nobody except employees were using "that version" of the software. Everyone else was using some slightly different version (if you counted each feature as a version), and there were thousands and thousands of versions in total.

We ended up creating just ~100 versions of our app (~100 experiment buckets), and then you could join a bucket. Teams could even reserve sets of buckets for exclusive experimentation purposes. We also ended up reserving a set of buckets that always got the control group.

You've approached it a different way, and probably a more sustainable way. It's interesting. How do you deal with the bias from your 'more courageous' people?

[adontz]

>> How do you deal with the bias from your 'more courageous' people?

That's a great question. We had no general solution for that. We tried to survey people, but results were inconclusive, not statistically significant.

[withinboredom]

I mean that "courageous" people are more likely to take risks and accept new features and thus probably more likely to be attracted to novelty (see: Novelty Effect) and require longer experiments to understand the actual impact.

[tauntz]

> At one point, we had so many experiments that literally nobody except employees were using "that version" of the software. Everyone else was using some slightly different version

Was this at Spotify by any chance? :)

[withinboredom]

No.

[vasco]

> User courage was 0.95 initially and could be updated manually. We tried to regenerate it daily based on surveys, but without much success.

Based on this ending, the courage bit sounds clever but is misguided. It adds complexity in a whole other variable, yet you have no way of measuring it or even do a good assessment.

I thought you were going to describe how you calculated courage based on the statistical usage of new features vs old features when exposed to them to update courage, meaning people who still keep using the product when it changes have more courage so they see more changes more often. But surveying for courage (or how easy they deal with change) is probably the worse way to assess it.

But even that I don't know what purpose would have because now you destroyed your A/B test by selecting a very specific sub population, so your experiment / feature results won't be good. I'm assuming here a product experimentation approach being used, not just "does it work or not" flags.

[adontz]

Mostly functional changes. Like deploying a new parser, which may not support all the old files. There were users which will contact customer support in panic stating that their life is ruined by this change and there were users who's like that fixed by next quarter.

[j45]

What’s important is if it worked for you and your audience.

There’s no standard requiring something to work for everyone, and it being less value if it isn’t.

[siva7]

Sounds like an overengineered solution to something that can be solved as simple as with a checkbox "i would like to get access to experimental features" in the UI.

[jasfi]

I'd go with that option too. I don't think users want to be surprised with being experimented on. Some users could take it worse than others.

[j45]

Getting one or a few new features is one thing, getting too many might be too much.

Some granularity and agency for the user is valuable. Maybe let them pick everything as a whole or a few features at a time.

[adontz]

I respectfully disagree. Depends on number and severity of experiments. Comparing two decimals is really not harder than checking a boolean, still a single "if". I do not see much over-engineering here.

[remram]

What does "tolerating experiments" mean? If they can tell it's an experiment, then isn't your change bad?

Do you mean "tolerate change"? But then you still eventually roll out the change to everyone anyway...

Or do you mean that users would see a different color for the "buy now" button every day?

From a purely statistical point of view, if you select users which "tolerate" your change before you measure how many users "like" your change, you can make up any outcome you want.

[j45]

I suspect it’s because some users will actually be pioneers and early adopters vs believing they are.

This kind of threshold adds some flexibility into the subjectivity of finding the best cohort to test a feature with.

[remram]

Where the best cohort to test with is the one that agrees with you...

You can call this measure "courage" but that is not actually what you are measuring. What you measure is not that different from agreement.

[j45]

I didn’t use the word courage, still I understand what you’re saying.

[remram]

adontz did above, that's what they called this user-tolerance-for-experiments metric. I didn't mean to imply you would too, apologies.

[j45]

Oh, no need to apologize at all.

I could have clarified as well that I was leaning more towards the user-tolerance... or as I like to call it user-guess that this feature might be OK with them :)

Another thing I like about granular and flexible feature flag management is you can really dial in and learn from which features get used by whom, actually.... instead of building things that will collect dust.

[inhumantsar]

I think you might be mixing things up a bit.

the tolerance score wouldn't be tied to a specific change. it's an estimate of how tolerant a person is of changes generally.

it's not that different from asking people if they want to be part of a beta testers group or if they would be open to being surveyed by market researchers.

targeting like that usually doesn't have a significant impact on the results of individual experiments.

[remram]

If only people who like changes like your change, should you really go ahead?

Plus you don't know what that correlates to. Maybe being "tolerant of changes" correlates with being particularly computer-savvy, and you're rolling out changes that are difficult to navigate. Maybe it correlates to people who use your site only for a single task, it would appear they don't mind changes across the platform, but they don't see them. Maybe it correlates with people who hate your site now, and are happy you're changing it (but still hate it).

You can't use a selected subset that is not obviously uncorrelated from your target variable. This is selection bias as a service.

[lucideer]

This seems like it would skew the data significantly for certain use-cases.

Unless you're feature flagging to test infra backing an expensive feature (in which case, in a load-balancer / containerised world, bucketing is going to be much a much better approach than anything at application level), then you most likely want to collect data on acceptance of a feature. By skewing it toward a more accepting audience, you're getting less data on the userbase that you're more likely to lose. It's like avoiding polling swing states in an election.

[vault]

From your naming, I would have done the opposite :) Start with courage 0.05 and show experiments whenever it is greater than the threshold. To enable a feature for everybody, you lower the threshold to 0.

How did you measure "experiment tolerance"?

[adontz]

Yeah, naming was bad. Courage 0 is maximum courage.

>> How did you measure "experiment tolerance"?

Feedback from CS mostly. No formal method. We tried to survey clients to calculate courage metric, but failed to come up with anything useful.

[skeeter2020]

This seems really complex, specifically in the area where I find product, CS & marketing least likely to want it: targeting and controlling their audience. Sounds like a cool thought experiment, fun and challening to implement and not really practical or useful.

If you have a huge userbase and deploy very frequently FFs are great for experiments, but for the rest of us they're primarily a way to decouple deploys from releases. They help with the disconnect between "Marketing wants to make every release a big event; Engineering wants to make it a non-event". I also find treating FFs as different from client toggles is very important for lifecycle management and proper use.

More than the binary nature I think the bigger challenge is FFs are almost always viewed as a one-way path "Off->On->Out" but what if you need to turn them off and then back on again? It can be very hard to do properly if a feature is more than UI, that might cause data to be created or updated that the old code then clobbers, or issues between subsystems, like microservices that aren't as "pure" as you thought.

[adontz]

Yes, it's not a good solution. Targeting was missing, good catch. I've just shared an unusual experience to inspire further experimenting.