Iterative development and design helps you to reach your way towards understanding what the customer really needs, to try out new ideas, evaluate designs, experiment, respond to feedback and react to changing circumstances. Everything gets better as you learn more about the domain and about the customer and about the language and technologies that you are using. This is important early in development, and just as important later as the product matures and in maintenance where you are constantly tuning and fixing things and dealing with exceptions.
But there are downsides as well. Iterative development erodes code structure and quality. Michael Feathers, who has been studying different code bases over time, has found that changes made iteratively to code tend to bias more towards the existing structure of the code, that developers make more compromises working this way. Code modules that are changed often get bigger, fatter and harder to understand.
Working iteratively you will end up going over the same ground, constantly revisiting earlier decisions and designs, changing the same code over and over. You’re making progress – if change is progress – but it’s not linear and it’s not clean. You’re not proceeding in a clear direction to a “right answer” because there isn’t always a right answer. Sometimes you go backwards or in circles, trying out variants of old ideas and then rejecting them again, or just wandering around in a problem space trying stuff until something sticks. And then somebody new comes in who doesn’t understand or doesn’t like the design, tries something else, and leaves it for the next guy to pick up. Changes in design, false starts, dead ends and flip flops leave behind traces in the code. Even with constant and disciplined refactoring, the design won’t be as clean or as simple as it would be if you “got it right the first time”.
It doesn’t just wear down the code, it wears down the team too
Iterative development also has an erosive effect on an organization’s memory – on everyone’s understanding of the design and how the system works. For people who have been through too many changes in direction, shifts in priorities and back tracking, it’s difficult to remember what changed and when, what was decided and why, what design options where considered and why they were rejected before, what exceptions and edge cases came up that needed to be solved later, and what you need to know when you’re trying to troubleshoot a problem, fixing a bug or making another design change.
Over the course of the last 6 or more years we’ve changed some ideas and some parts of the code a dozen times, or even dozens of times, sometimes in small, subtle but important ways, and sometimes in fundamental ways. Names stay the same, but they don’t mean what they used to.
The accumulation of all of these decisions and changes in design and direction muddies things. Most people can keep track of the main stories, the well-used main paths through the system. But it’s easy for smart people who know the design and code well, to lose track of details, the exception paths and dependencies, the not-always-logical things that were done for one important customer just because 25 or 50 or 110 releases ago. It gets even more confusing when changes are rolled out incrementally, or turned on and off in A/B testing, so that the system behaves differently for different customers at different times.
People forget or misremember things, make wrong assumptions. It’s hard to troubleshoot the system, to understand when a problem was introduced and why, especially when you need to go back and recreate a problem that happened in the past. Or when you’re doing trend analysis and trying to understand why user behaviour changed over time – how exactly did the system work then? Testers miss bugs because they aren’t clear about the impact of a change, and people report bugs – and sometimes even fix bugs – that aren’t bugs, they’ve just forgotten what is supposed to happen in a specific case.
When making changes iteratively and incrementally, people focus mostly on the change that they are working on now, and they forget or don’t bother to consider the changes that have already been made. A developer thinks they know how things work because they’ve worked on this code before, but they forget or don’t know about an exception that was added in the past. A tester understands what needs to be tested based on what has just been changed, but can’t keep track of all of the compatibility and regression details that also need to be checked.
You end up depending a lot on your regression test suite to capture the correct understanding of how the system really works including the edge cases, and to catch oversights and regression mistakes when somebody makes a fix or a change. But this means that you have to depend on the people who wrote and maintained the tests and their understanding and their memory of how things work and what impact each change has had.
Iterative development comes with costs
It’s not just the constant pace, the feeling of being always-on, always facing a deadline that wears people down over time. It’s also the speed of change, the constant accumulation of small decisions, and reversing or altering those decisions over and over that wears down people’s understanding, that wears down the mental model that everyone holds of how the system works and how the details tie together. All of this affects people’s accuracy and efficiency, and their confidence.
I am not sure that there is a way to avoid this. Systems, teams, people all age, and like in real life, it’s natural that people will forget things. The more changes that you make, the more chances there are for you to forget something.
Writing things down isn’t much of a help here. The details can all be found somewhere if you look: in revision history, in documentation, in the test suite and in the code. The problem is more with how people think the system works than it is with how the system actually works; with how much change people can keep up with, can hold in their heads, and how this affects the way they think and the way they work.
When you see people losing track of things, getting confused or making mistakes, you need to slow down, review and reset. Make sure that before people try to fix or change something they have a solid understanding of the larger design – that they are not just focusing on the specific problem they are trying to solve. Two heads are better than one in these cases. Pair people up: especially developers and testers together, to make sure that they have a consistent understanding of what a change involves. Design and code reviews too, to make sure that you’re not relying too much on one person’s memory and understanding. Just like in real life, as we get older, we need to lean on each other a bit more.
So are you saying that software rot is inevitable, as we used to think it was before refactoring was invented?
The more you change the design and code, the more traces you leave behind. Refactoring helps, but unless you’re refactoring perfectly (and you can only do this if you perfectly understand what the new design will be, which you usually won’t because that’s the point of iterative and incremental design), it can’t clean up all the evidence.
I think agile development is being misunderstood and misused. The output of each iteration should be features that are implemented, tested, ready to be used. Those features are not to be revisited and refactored on next iterations, save for requirements change. If you have your requirements changed and need to refactor a previously released feature (which happens all the time), well, it’s a change and they exist even when we don’t use agile. In case your changes come from misunderstood requirements, that’s a problem with your requirement analysis. The way it’s been put here, agile sounds more like a “go… Read more »
“…the feeling of being always-on, always facing a deadline…”
Isn’t this the reason why the agile thing were created?
By making the engine works on the red line you are expecting to see more performance. It’s a trade off, you let the other concerns aside.
In my opinion there are serious problems with the current adoption of Agile practices:
http://effectivesoftwaredesign.com/2014/03/17/the-end-of-agile-death-by-over-simplification/