User testing with Waffle

Testing a feature (i.e. not testing the code) with users usually takes one of two forms: small-scale tests with individuals or known group, and large-scale tests with a subset of production users. Waffle provides tools for the former and has some suggestions for the latter.

Small-scale tests

There are two ways to control a flag for an individual user:

  • add their account to the flag’s list of users, or
  • use testing mode.

Testing mode makes it possible to enable a flag via a querystring parameter (like WAFFLE_OVERRIDE) but is unique for two reasons:

  • it can be enabled and disabled on a flag-by-flag basis, and
  • it only requires the querystring parameter once, then relies on cookies.

If the flag we’re testing is called foo, then we can enable testing mode, and send users to oursite.com/testpage?dwft_foo=1 (or =0) and the flag will be on (or off) for them for the remainder of their session.

Warning

Currently, the flag must be used by the first page they visit, or the cookie will not get set. See #80 on GitHub.

Researchers can send a link with these parameters to anyone and then observe or ask questions. At the end of their session, or when testing mode is deactivated, they will call back to normal behavior.

For a small group, like a company or team, it may be worth creating a Django group and adding or removing the group from the flag.

Large-scale tests

Large scale tests are tests along the lines of “roll this out to 5% of users and observe the relevant metrics.” Since “the relevant metrics” is very difficult to define across all sites, here are some thoughts from my experience with these sorts of tests.

Client-side metrics

Google Analytics—and I imagine similar products—has the ability to segment by page or session variables. If you want to A/B test a conversion rate or funnel, or otherwise measure the impact on some client-side metric, using these variables is a solid way to go. For example, in GA, you might do the following to A/B test a landing page:

ga('set', 'dimension1', 'Landing Page Version {% flag "new_landing_page" %}2{% else %}1{% endif %}');

Similarly you might set session or visitor variables for funnel tests.

The exact steps to both set a variable like this and then to create segments and examine the data will depend on your client-side analytics tool. And, of course, this can be combined with other data and further segmented if you need to.

Server-side metrics

I use StatsD religiously. Sometimes Waffle is useful for load and capacity testing in which case I want to observe timing data or error rates.

Sometimes, it makes sense to create entirely new metrics, and measure them directly, e.g.:

if flag_is_active('image-process-service'):
    with statsd.timer('imageservice'):
        try:
            processed = make_call_to_service(data)
        except ServiceError:
            statsd.incr('imageservice.error')
        else:
            statsd.incr('imageservice.success')
else:
    with statsd.timer('process-image'):
        processed = do_inline_processing(data)

Other times, existing data—e.g. timers on the whole view—isn’t going to move. If you have enough data to be statistically meaningful, you can measure the impact for a given proportion of traffic and derive the time for the new code.

If a flag enabling a refactored codepath is set to 20% of users, and average time has improved by 10%, you can calculate that you’ve improved the speed by 50%!

You can use the following to figure out the average for requests using the new code. Let \(t_{old}\) be the average time with the flag at 0%, \(t_{total}\) be the average time with the flag at \(p * 100%\). Then the average for requests using new code, \(t_{new}\) is...

\[t_{new} = t_{old} - \frac{t_{old} - t_{total}}{p}\]

If you believe my math (you should check it!) then you can measure the average with the flag at 0% to get \(t_{old}\) (let’s say 1.2 seconds), then at \(p * 100\) % (let’s say 20%, so \(p = 0.2\)) to get \(t_{total}\) (let’s say 1.08 seconds, a 10% improvement) and you have enough to get the average of the new path.

\[t_{new} = 1.2 - \frac{1.2 - 1.08}{0.2} = 0.6\]

Wow, good work!

You can use similar methods to derive the impact on other factors.