Tag Archives: software economics

Sushi, Hibachi and Other Ways of Serving Software Delicacies

Let’s say you own a restaurant. Quite a large restaurant. You’ve hired a manager to run the place for you because you are about to take the fruits of your success and invest in opening three more around the country. You leave the manager with a budget to make any improvements he sees fit.

After being away for a few weeks, you return to your restaurant. It’s very busy thanks to the great reputation you had built for it. The kitchen has some new state of the art equipment and you can see that there are a lot of chefs and kitchen-staff preparing meals furiously. Obviously, with a restaurant this full you have to prepare a lot of meals.

Too many chefs?

But then, you notice that there are only a few service staff taking orders and delivering the meals to the customers. Tables are waiting a long time for their meals. You notice the service staff spending a lot of their time taking meals back to the kitchen because they have gone cold waiting so long to be served. You also overhear several customers complaining that the meal they received was not the one they ordered, increasing the demand for the preparation of more meals. You call the manager over and ask him what’s going on… he says “because we need to invest more in the kitchen”. You wonder why. He tells you “because there are so many meals to prepare and we can’t keep up”.

At this point, it’s quite obvious that the constraint is not through lack of investment in the kitchen. Instead the lack of investment is in taking the right orders and getting the meals to the customers. To solve this, I think most people would increase the investment in getting the order right and in how the meals get to the customer not by investing more into the kitchen.

Obvious, right? Despite this, time and again, I see organisations happy to grow their investment in more programmers writing more code and fixing more bugs and all but ignore the end-to-end process of finding out what people want and taking these needs through to working capabilities available to the customer. Business analysis, user-experience, testing and operations all seem to suffer in under investment. If it takes you four weeks to write some code for several new features and then another four weeks for that to become capabilities available to your customer, hiring more programmers to write more code is not going to make things any better.

Sushi and Hibachi

So, how would you advise the restaurant? In the short term, you can get some of the chefs to serve customers while you hire more service staff. Another solution is to invest in infrastructure that brings the meals closer to the customer, such as in hibachi restaurants where the chef takes the order and cooks the meal at the table or in sushi restaurants where meals are automatically transported via conveyor belt.

In software teams, we can bring the preparation of features closer to our customers. We can involve a cross-functional team in the conversation with the customer (such as the chef being at the customer’s table). We can also automate a large proportion of the manual effort of getting features from a programmer’s terminal into the hands of the customer (as in with the conveyor belt). And, there is so much we can automate.

Integration, scripted test execution, deployment and release management can largely be automated. Since speeding up these tasks has the potential to increase the throughput of an entire development organisation, it seems appropriate to invest in these activities as we would in automating any other significant business activity – such as stock-control or invoice processing.

Cooking for ourselves

Why not ask a good number of our programmers to take some time out from automating the rest of the business and automate more of our own part of the business? Taking this approach seems to be the exception rather than the rule. I see organisations having one or two dev-ops folks trying to create automated builds; and similar number of testers, with next to no programming experience, trying to automate a key part of the business – regression testing. And then asking them all to keep up with an army of developers churning out new code and testers churning out more scripted tests.

Serving up the capabilities

Instead, let’s stop looking so closely at one part of the process and look more at the whole system of getting from concept to capability. Let’s invest in automating for ourselves as much as we automate for others – and let’s dedicate some of our best people to doing so.

In short, it’s not just about the speed at which we cook up new features. It’s as much about the speed at which we can serve new, working capabilities to our customers.

Old Favourite – Sharks, Debts, Critical Mass and other reasons to Sustain Quality

This article originally featured on my old blog on 18th January 2010.

A while back I tweeted about critical mass of software:

Critical Mass of Code – past which the changeability of the code is infeasible, requiring that it be completely rewritten.

An elaboration of this might be:

Critical Mass of Software: the state of a software system when the cost of changing it (enhancement or correcting defects) is less economical than re-writing it.

This graph illustrates a hypothetical project where the cost of change increases over time (the shape of which reminds me of a thresher shark):

Note:The cost of a rewrite gradually grows at first to account for the delay between starting the re-write and achieving the same amount of functionality in the original version of the software at the same point in time. The gap between the cost-of-change and the cost-of-rewrite begins to decrease as the time it takes to implement each feature of comparable benefit in the original version grows.

It was just one of those thoughts that popped into my head. I soon discovered that critical mass of software is not a new idea.

One opportunity that I feel organisations miss out on is that this can be used as a financial justification for repaying technical debt, thus preventing or delaying the point at which software reaches its Critical Mass, or avoiding it altogether.

Maintaining quality combined with practices that keep the effort involved in completing each feature from growing over time can defer critical mass indefinitely:

Note: The cost of a rewrite is slightly less at first assuming that we are doing a little extra work to ensure that we get the infrastructure we need up and running to support sustainable change (e.g. continuous integration etc.)

This flat cost of change curve is one of the motivations of many agile practices and software craftsmanship techniques, such as continuous integration, test-driven-development, behaviour driven development and the continuous refactoring inherent in the latter two. A flat cost-of-change trend is good for the business, as they have more predictable expenditure.

The behaviour I’ve noticed with some teams is to knowingly accrue technical debt to shorten time to a near-future delivery in order to capitalise on an opportunity and then pay it back soon after:

This certainly defers reaching critical mass but it doesn’t prevent it.

Unfortunately, I see more teams taking on technical debt and not repaying it (for various reasons, often blamed on “the business” putting pressure on delivery dates).

If we had a way of estimating the point at which we’d reach critical mass, we’d be able to compare the benefit of repaying technical debt now in order to avoid or delay the cost of a rewrite… or even better, demonstrate the value of achieving a flatter cost of change.

I’m not sure that we have the data or the means of predicting it, but perhaps these illustrations of a hypothetical scenario will help you explain why sustaining constant quality, automating repeatable tests (or checking as some prefer to call it) wherever technically possible and, on those occasions when we do need to make a conscious choice to compromise quality to capitalise on an opportunity, that the debt is repaid soon after.

One company I know reached this point a couple of years ago and embarked on a department wide transformation to grow their skills in agile methods so that they not only re-wrote the software but could also avoid ever reaching critical mass again. A bold move, but one that paid off within a year. Even for them, they’ll need to avoid complacency as time goes on and not forget the lessons of the past. Let’s hope they do.