Monday, August 31, 2009

Representing Design Rationale Inside Activity Diagrams

Design is about making choices. We often do so on the fly, leaning on experience and intuition, by talking about the problem with colleagues, or borrowing from literature (e.g. patterns). We also make some choice by habit, which is a different form of experience, one that has higher risk of becoming disconnected with the real problem.
Most of this process is tacit, and even when we discuss choices openly, it doesn't get recorded. Sometimes, a list of pros/cons is made when there is some disagreement about the best option.

This all works well when the problem is simple, but sometimes even experienced designers feel like they're not grasping the essential issues, that something has not yet been found, named, disentangled. This is when having yet one more tool can prove useful.
Now, I don't usually go through the effort to model and transcribe the rationale behind each and every design choice I make. It could be interesting, also from a pedagogical point of view, but it would take a lot of time and would probably disrupt my thought processes. However, when the issues are particularly thorny/unclear, or when there is a large disagreement on the best choice (or even on the goals and criteria), I've found that getting design rationale out of our individual heads and talk on a shared representation can move things a little forward.

Over the years, I've tried out a number of tools, approaches, and so on; lately, I've tried using Activity Diagrams in a rather unorthodox way, to represent my reasoning about design, not design itself. The idea is not to encode your decisions ex-post, but ex-ante, while you're thinking (that is, while they're still options, not decisions). Also, the diagram must be considered quite fluid, as it shows our current understanding, and we're building the diagram to improve our understanding.

Enough talk, let's see a realistic example. I'll refer to the Large Display problem I discussed a few months ago. Actually, I'll just cover the initial choice between using a real-time database or an IPC/messaging system. It's gonna be quite a mouthful anyway!

To start, I'll have to draw a line between a messaging system and a RTDB, and that in itself is not easy. I'll go for a very simple distinction, because my goal here is not really to talk about RTDBs, but about design rationale (the usual "look at the moon, not at the finger" concept).
So, consider a control system that reads some data from the field and then needs to publish those data for other processes. It could just send data through a messaging (publish/subscribe) system. Here I define a messaging system as stateless, meaning it simply keeps track of subscriptions, and sends everything that is published to the subscribers (according to some criteria, like message type or tag). It does not keep an history, or a snapshot of what has been last sent. Therefore, it cannot apply some filters, like "notify me only if the difference between the previous value and the current value is above a threshold" because the previous value is just not stored. Also, when a subscriber is started, it cannot get the current snapshot of the system, because it is not there: it will have to wait for messages to come, incrementally. Shortly stated, a RTDB will keep a snapshot of the system, and well, you can figure out the difference. Of course, a RTDB is also more complex.

So, how do we choose between a messaging system and a RTDB? We may write down a long list of pro/cons, but that's really unstructured, and that's not the way our brain works. To provide more structure, I use an Activity Diagram with orthogonal swimlanes (all the following pictures are taken from Star UML, a free tool that is rather fast and unobtrusive).
The vertical swimlanes are flexible: they represent the main concerns. The horizontal swimlanes are fixed: they provide structure.

For the Large Display problems, we could start with a few main concerns like performance, reliability, cost, and so on. We just drop the names on the vertical swimlanes. My template is then partitioned in 3 horizontal bands: the root question, the reasoning, the outcome. Everything inside is dynamic, and changes as we understand more: even the root questions may change, as we discover larger or smaller, independent problems. Sometimes, even the main concerns change, as we discover options or issues we didn't consider before.

We can focus on just one concern right now, let's say performance (don't we all like performance? :-). A first-cut, interesting top-level question could be: is the published data rate high or low? If the rate is low and we have no persistent state, when you turn on the large display you see nothing: you have to wait till some data gets published. On the other hand, if the rate is high, it may even overwhelm the display system: there is little need to refresh a value a thousand times per second. That actually depends on the display: if it's a real-time plot, you may want a high refresh rate too.

Ok, we could start modeling this part of our reasoning using the familiar activity diagram symbols. Actually, since most of the nodes here would be decision nodes, I just omit the diamond and use an activity node with multiple outgoing paths to show choices.

Note: The empty boxes are just placeholders for some later reasoning. It's just laziness on my side :-) and they wouldn't appear in a real diagram.

Now, this seems just like a decision tree, but it's slightly different. First, it's a decision graph: common choices between paths are shared, and this is a precious information because it shows crucial choices (more on this later). Second, it's a multifaceted graph: every vertical swimlane shows a facet of a more complex reasoning; for instance, what is good for performance might not be good for reliability or cost.

Let's try to move ahead a little. When the incoming data rate is higher than what [most] clients need, we have basically two choices:
1) smarter subscriptions; they could still be rather dumb, like "no more than 3 times per second" or much smarter like "when relative change is higher than 5%, but no more than 5 times per second". Note that the latter is more suited to a RTDB than to a stateless messaging system.
2) change paradigm and move to client-initiated polling. The clients will ask for data with their own timing. Of course, at this point we give up the possibility of not asking for data if the value has not changed. Anyway, this again requires some kind of stateful middleware; a messaging system won't do.
When data rate is low, but high startup time for clients is not an option, we can't wait for data to come: we have to poll, at least at startup. So, polling can solve two problems, of course at expense of bandwidth if it is the only available option.

While drawing this, we may come to the conclusion that we need to ask better questions: are we building a publisher-driven or a client-driven system? If it's client-driven, it cannot be stateless! What do we really know about clients? How many there will be? What about publishers? What is the typical data rate and configuration? What are we aiming for? Do we need to narrow the expectations? This might change the top question (client Vs. publisher driven) or even some concern. That's fine, it means the technique is working :-) and that it's helping us thinking.

Now, it would take quite a lot of time to explore all the facets of even a simple system like this. Actually, most people won't even do it in real life: they will fall in love with one idea, spend most of their time preaching and rationalizing about the virtues of their idea, and never really take the time to go through this kind of process. Still, trying to work out the "Reliability" swimlane would prove interesting. For instance, a common technique to achieve reliability is redundancy. Redundancy is much easier for a stateless system. Redundancy is easier when clients don't have to subscribe at all, but can simply poll. And so on. If you have some spare time, you may want to give it a try.

The notation I use is quite informal. I could improve that easily: UML is fairly flexible; so far I didn't, because people can grasp it anyway, even when I drop in the < < or > > to represent options or when I have just one arrow coming out, meaning that I've just decomposed a choice and a consequence. It's just a reasoning workflow, and I haven't felt the need to make it any more precise than that.

Back to the forcefield: the rationale is not the forcefield. The rationale, however, is talking about forces and centers. Outcomes (messaging and RTDB) are centers. Main choices, like "client driven" or "stateless", are again centers. Those centers are attracting or rejecting each other. This is the forcefield. This is closer to the way I think in the back of my mind, how I "see" the system, how I keep options open. Now, I just need a way to show this. That's for my next post :-).

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