The case against #NoEstimates, part 3: NoEstimates arguments and their weaknesses

I’ve spent the last two blog posts introducing the #NoEstimates movement, first discussing what it appears to espouse, and presenting some initial reasons why I reject it. I then covered the many solid reasons why it makes sense to use estimates in software development.

This time, let’s go through, in detail, the various arguments put forward commonly by the NoEstimates advocates in their opposition to estimates and in their explanation of their approach. Full disclosure: I’ve attempted to include the major NoEstimates arguments, but this won’t be a balanced presentation by any means; I find these arguments all seriously flawed, and I’ll explain why in each case.

Here we go, point by point:

  • “Estimates aren’t accurate, and can’t be established with certainty”

Let’s use Ron Jeffries’ statement as an example of this stance:

“Estimates are difficult. When requirements are vague — and it seems that they always are — then the best conceivable estimates would also be very vague. Accurate estimation becomes essentially impossible. Even with clear requirements — and it seems that they never are — it is still almost impossible to know how long something will take, because we’ve never done it before. “

But “accurate” is simply the wrong standard to apply to estimates. It’d be great if they could be totally accurate, but it should be understood at all times that by nature they probably are not. They are merely a team’s best shot, using the best knowledge available at the time, and they’re used to establish an initial meaningful plan that can be monitored and adjusted moving forward. They’re a tool, not an outcome. As such, the benefits of estimates, and their contributions to the planning and tracking process, exist even without them being strictly “accurate” per se. These benefits were itemized in my last post.

Knowing the future precisely isn’t what estimating is about, actually. It’s a misunderstanding and a disservice to think it is. Here’s why. [Read more…]