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About Spikes A spike solution, or spike, is a technical investigation. It's a small experiment to research the answer to a problem. For example, a programmer might not know whether Java throws an exception on arithmetic overflow. A quick ten-minute spike will answer the question.

Performing the Experiment The best way to implement a spike is usually to create a small program or test that demonstrates the feature in question. You can read as many books and tutorials as you like, but it's my experience that nothing helps me understand a problem more than writing working code. It's important to work from a practical point of view, not just a theoretical one.

Writing code, however, often takes longer than reading a tutorial. Reduce that time by writing small, standalone programs.

In its common usage, evolutionary design is a disaster. The design ends up being the aggregation of a bunch of ad-hoc tactical decisions, each of which makes the code harder to alter. In many ways you might argue this is no design, certainly it usually leads to a poor design. As Kent puts it, design is there to enable you to keep changing the software easily in the long term. As design deteriorates, so does your ability to make changes effectively. You have the state of software entropy, over time the design gets worse and worse. Not only does this make the software harder to change, it also makes bugs both easier to breed and harder to find and safely kill. This is the "code and fix" nightmare, where the bugs become exponentially more expensive to fix as the project goes on

the planned design approach has been around since the 70s, and lots of people have used it. It is better in many ways than code and fix evolutionary design. But it has some faults. The first fault is that it's impossible to think through all the issues that you need to deal with when you are programming. So it's inevitable that when programming you will find things that question the design. However if the designers are done, moved onto another project, what happens? The programmers start coding around the design and entropy sets in. Even if the designer isn't gone, it takes time to sort out the design issues, change the drawings, and then alter the code. There's usually a quicker fix and time pressure. Hence entropy (again).

One way to deal with changing requirements is to build flexibility into the design so that you can easily change it as the requirements change. However this requires insight into what kind of changes you expect. A design can be planned to deal with areas of volatility, but while that will help for foreseen requirements changes, it won't help (and can hurt) for unforeseen changes. So you have to understand the requirements well enough to separate the volatile areas, and my observation is that this is very hard. Now some of these requirements problems are due to not understanding requirements clearly enough. So a lot of people focus on requirements engineering processes to get better requirements in the hope that this will prevent the need to change the design later on. But even this direction is one that may not lead to a cure. Many unforeseen requirements changes occur due to changes in the business. Those can't be prevented, however careful your requirements engineering process.