The gist of this article is good. Fixing the code is often the smallest part of rolling out a bug fix. That's extremely important to be aware of. Using historical data can be a great way of seeing how fast you can actually roll out changes.

But I don't get how they went from that to "estimations are bad". It seems to me that they're just doing better estimations now by looking at historical data?

> I could tell confusion from the look of my team lead. How could I say it would take over five days for a change that we estimate to take 10 minutes!

Isn't this like comparing apples and oranges? The estimation of 10 minutes is clearly "the time it takes to flip the flag", but what they actually want to estimate is "the time it takes to make it into production in a safe manner".

Maybe the team leader is confused because they thought this was a man-hour estimation? And they're now afraid that one developer will work full-time five days on it?

> On Monday, we started with the stand-up and discussed picking up this task. Another engineer proposed fixing a bug on another component first before turning on the flag.

Doesn't this imply that their 10 minute estimation was done completely without asking the team?

> As humans, it is impossible to consider all the potential complexity. […] We did not consider a legacy system with bugs and scaling problems. […] We did not consider a combined release process with a Release Manager.

Really? You couldn't consider the complexity of a release process you designed yourself? You honestly thought this was a 10 minute task even though you knew you were dealing with a legacy system?

We had a much more primitive version of this for our product roadmap. After working with mostly the same dev team for a few years, we felt that in a given year we seem to consistently be able to complete X “big” projects, Y “mediums” and Z “smalls” despite all the random stuff that invariably pops up.

So that list was the spine of our annual planning, the stuff that we really prioritized. The rest of the stuff that people wanted to do would have to fall in between the gaps of that master set.

It worked surprisingly well for us, but we had to build this group experience first. After a few years with another dev team at another company, I brought it up as an alternative to over ambitious roadmaps and it worked well there too.

How much of this was just due to more team familiarity and experience or this oversimplified process? Don’t know. But it felt like the roadmap trade offs were more thoughtful, devs felt more relaxed and had reserves if we had to red line a bit, and fewer missed commitments.

The detachment of individual deliverables from estimates is definitely an improvement but I honestly think the notion of even hand waving estimation is a bad idea with harmful consequences.

Measure velocity for sure. Keep track of what kinds of things harm or improve it. But the absolute worst thing you can do is find an abstraction like tshirt size or rolling average and superimpose it on discreet work. You’re going to be wrong. And probably disappointed or disappoint someone else.

My strategy for estimation is:

- It’s tiny and I’m doing it now, I can give you an estimate within an hour margin.

- It’s small, it’ll be done in a reasonable amount of time that no further estimation is required.

- It’s not small and more research/design/planning is required. Any estimate you extrapolate from this is dishonest.

That’s it.

Edit: that wasn’t quite all. “It’s tiny” is reserved for urgent situations that require rapid response and not something I offer outside that situation. For daily work, it’s small or it’s too big to say. Tiny things are too easy to abuse (by managers or engineers) if they’re part of the normal flow.

The benefits described here are achieved by ANY abstracted estimation system. Ask people to estimate in units of difficulty, complexity, cups of coffee, whatever. Keep those units consistent relative to each other (a 4 cup task looks roughly twice as big as a 2 cup task, in advance). Track the relationship between units and hour/days over several sprints, and your average automatically factors in all the external things like team collisions, illness, developer downtime, refactoring, bugfixes, release process, etc. The research shows that the law of large numbers makes this MUCH more accurate than pure time estimation. (time estimation is extremely difficult to get correct more than about 35% of the time; averages of consistent units are correct enough to run a casino budget).

The scrum people will tell you to call this unit "story points", but it doesn't matter what you call it, as long as it's based on something connected to task duration. Difficulty, complexity, risk, whatever.

Note that if you're tracking the conversion rate between developer estimated hours and real hours, you're already doing this. But you'll get an increased accuracy by using units that are explicitly not time-related, just because of quirks in how human brains think about time.

In my experience, you need both numbers. A customer naturally wants to know when a change arrives in production. I call this "latency", you call it cycle time, but maybe there is a small difference because I also try to include the time it takes to even start development. But a customer also wants to know about effort, often. Because usually effort is what gets billed. So a 10min (realistically, the bill we be about an hour, won't it ;) ) change has a five day latency, which is actually a relatively fast pace for a legacy system with many different stakeholders.

I am the author of the story. I appreciate any feedback so that I learn from you! I will be participating in the discussion.

I feel like I'm fulfilling a stereotyped HN commenter role by asking this, but isn't "prediction based on historical data" actually, you know, an estimate?

What works well for me:

1. Split the project into tasks. (a task taking at least 3-5 PTs)

2. Have a POC / prerelease phase

3. For complex tasks introduce a second polishing task, like "Basic User management 1/2" and later "Basic User management 2/2". If the second one isn't needed: Great!

4. For all tasks/areas estimate a lower and an upper estimation in hours or PT.

5. Revisit your estimation afterwards using this process:

5.1. Imagined that someones gives you the following choice: You get 1000$ if your the true value lies within your estimation 90% of the time. Or you can roll a dice and in 9/10 you get the 1000$.

5.2 If confidently pick your estimation ("I'm 100% sure the true value is within my bounds") then it's too wide.

5.3 If you rather pick the dice-roll, then you don't trust your estimation, it's too narrow.

5.4 Adjust your estimation until both options seem equally probable to you: dice-roll or your estimate being true 90% of the time.

I am still not clear at all how "cycle time" is a meaningful measure without correcting for size of the feature.

I'm also confused as to how you can look at "mean of one week, with a standard deviation of two weeks" and not wonder about the asymmetry of the distribution, and if mean/sd are really the metrics of choice here. (I'd think 1st/3rd quartile are a better choice, because it gives some clue as to skewedness)

Sounds more like a case for using cycle time as a checksum to make sure the team’s estimates are fully considered.

They’re clearly missing 4 days of standard requirements in their estimates.

I couldn't read all the text, but I believe the metapoint is that the need for accurate estimates drop as the cycle time is reduced.

It is all about feedback.

My analogy is an old analog style joystick that positions a simulated robot arm on a screen. If the update rate is high enough I can track a rapidly moving dot, but if it drops across some threshold, and I needed to accurately position the arm at some time in the future, then I would need to construct a huge model of the system, know the force, stiction, friction, mass, moment and thermal expansion. (edit, I have another analogy, anyone can spray a moving target with a hose, but using a bow and arrow requires skill and practice)

Feedback allows us to use unpredictable components to make predictable systems. Those systems are nearly always amplifiers. Systems that use direct feedback don't have to have the same reductionist model as something that needs better prediction (estimates).

This is why Lisp was a super power in the 80s, it had a repl. Same as Smalltalk, the IDE and repl and the universe were all the same thing. It makes total sense that agile came out of a system based around repls and instant feedback. Arduino did it for embedded dev. Hypercard for programming, the spreadsheet before that.

Highbandwidth feedback allows us to be less skilled. Good estimators need to be highly skilled to make those estimates. Hose vs arrow. That reminds me, have you seen a really skilled FPS player on a predictable but high ping connection? They are almost timeless in how they predict the future, and to everyone else they dance between every 10th frame. Amazing predictors!

You can't agile a martian probe (yet). As new ways are discovered to reduce cycle times, the time between cause and effect, each proceeding structure of feedback is replaced with an even higher bandwidth one. Robust DFU is a metarepl as hardware manufactures race to ship products that are literally not finished and require a firmware update on boot to even function.

I think the type of estimation meant here is the agile type, where the team looks at a feature/requirement and estimates the time or relative effort based on the nature of the requirement + whatever other factors they choose. It's usually more intuitive and not based on historical data.

he also links to a conference talk about this. "you can make a claim that estimates are based on past behavior, but the fact is that what you're implementing is something that hasn't been implemented before. So any kind of measurement that you've made of something that has happened in the past is not going to impact what you're doing now" https://www.youtube.com/watch?v=QVBlnCTu9Ms

Thank you for your question!

Yes, I try to make the distinction between prediction looking at historical data and estimation asking the engineers how long it will take.