Talk About Quality

As good as the misconceptions surrounding Toyota make it sound, the truth is even better.

– Stewart Anderson in Quality Digest’s Quality Insider 20-Oct-2009

Thanks to Stewart Anderson, both for bringing us the real story about how Toyota, famed for focus on quality, actually goes about doing it. Put aside for a moment what you know about ISO, QMS, metrics etc and read the carefully crafted wisdom of experience. How quality really works at Toyota. And yes, you really can apply these ideas elsewhere, even in software development.

All quotes are from the linked article by Stewart Anderson.

Toyota’s basic pattern for improving a process is based on a simple three-part model:

1. Understanding the current condition.
2. Developing and defining a target condition.
3. Understanding and tackling problems which need to be overcome to move from the current condition to the target condition.

This model has learning at its heart [to identify] actions to solve problems in the current condition.

The primary responsibility [of the team leader] is to monitor the process, ensure that standard work is being followed, and coach and mentor the work team in improving the process. Team leaders receive special training in process improvement and problem solving ….

To read the full article click here.

Related reading: Gemba Kaizen: A Commonsense, Low-Cost Approach to Management (my review here).

Full disclosure: My Toyota is over 15 years old and still running fine. My last car was also a Toyota.

A new project is ramping up and needs more resources. Another project is winding down — maybe it can do with less.

Let’s move some people from one team to the other

What’s wrong with this common resource allocation decision? It’s not the decision. Moving resources to the project that needs them makes sense. What’s wrong is the words, which hide a false assumption.

Here, “team” means location. Move people from one place to another. Like conductors putting people on a train: the “teams” are two train stations. One called “Project A Station” and the other one, “Project B Station”.

But teams are not train stations. A team is not the room it sits in, or its box on the org chart. A team is the particular combination of people — their skills and personalities — and their shared experience. They know their project or product best right now, and equally important, they know each other and the special role each person fills. Reassigning people from one team to another is more than moving a person. It is actually dismantling the first team, and disturbing the second. The movement of people based on the train-station model of teams leads to a contradiction: both teams — origin and destination — cease to exist.

If we must model people and treat them as resources, a better model is a hand at cards. The managers are the players and partners; the game is getting more projects completed. There’s a fixed deck — the “human resources” of the company. Some cards may be similar in strength or level, but every card is unique.

The value of your hand is based entirely on the combination of cards you’re holding right now. If you or your partners need a stronger hand, everyone knows that some cards will have to be exchanged. Put down 2, pick up 2. I pick up what you put down. Resources. Cards. I know this doesn’t sound any better to workers than “counting heads”, but it is. Because every card player thinks twice before putting down a good card, and prefers to take over an entire good hand if they can.

That’s right, not “development” but “developer”.

The latest issue of The Embedded Muse newsletter—#179 (pdf)—has a reference to an interesting and very different kind of book about C. It’s about the business pressures and the thought processes of C software developers. And a lot more. It’s also long—1600 pages! So download a copy and skim for what interests you:

The New C Standard: An Economic and Cultural Commentary

Some lines that caught my eye, just in the first 100 pages, skimming with the “Page Down” key:

“Software developer interaction with source code occurs over a variety of timescales [for example, those] whose timescale are measured in seconds.”

“The act of reading and writing software has an immediate personal cost.”

“Source code faults are nearly always clichés; that is, developers tend to repeat the mistakes of others and their own previous mistakes.”

“A list of possible deviations should be an integral part of any coding guideline.”

“The following are different ways of reading source code, as it might be applied during code reviews …”

Other related references from the book’s author Derek M. Jones (http://www.knosof.co.uk/):

• Blog: http://shape-of-code.coding-guidelines.com/
• Article:  Coding Guidelines: Fact and Fiction
• Article: The 7±2 Urban Legend (small pdf)

And somehow related, from another author, Les Hatton (http://www.leshatton.org/):

• Article: Testing the value of checklists in code inspections

“Isn’t it nice to know that, when all else fails us, we have an innate decision-making tool to fall back on?”

Robert L. Glass, “Intuition’s Role in Decision Making” (IEEE Software, January/February 2008)

Yes, Glass admits, estimates that come unbidden from a manager’s subconscious seem the very opposite of quantitative or rational. But most decision-making methods have a common theme: using historical data to decide what to do next. Quantitative estimation puts everything out in the open. Rational support for a desired deadline is at least based on the facts. Intuition, at the other extreme, pulls that data, informally known as “experience”, from the subconscious. And since a good manager has experience, intuition works.

Sort of.

What’s hidden by Glass’ essay are the unstated quality standards that justify each method. Only a complete, quantitative estimate, matched by continuous measurement and adjustment, can promise high quality by the target date. Intuition, on the other hand, even coming from an experienced manager, makes quality likely, but hardly guaranteed. His successes are what keep him in his job, and his few failures are forgiven. Rightly so. He is employed to deliver good enough software, quickly, to meet modern society’s ever-growing appetite for computerized life.

So should we favor intuitive, seat-of-the-pants estimating, with its benefit of early delivery, but its cost of uncertain quality?

Digging deeper, we find that there are some very different reasons why we write software, which strongly influence how we plan our work.

1. Profit
2. Functionality
3. Beauty

Anyone who gets paid for writing commercial software must acknowledge that profit pays his or her salary. Profit leads eventually to pretty good quality, via competition. In the short run, though, we experience a lot of pressure, a lot of errors, and many customer-accepted (if sometimes societally unacceptable) failures. In that context, it seems, we should estimate quickly and intuitively, but admit that quality is expendable.

In a well-funded, non-profit organization (e.g. NASA, famed for error-free software — wholly separate from failure-free flights), software can be about complete functionality. Take as much time as needed to implement everything, perfectly. After all, when the spaceship flies past Jupiter, there’s no second chance.

Where, then, is guaranteed perfection, estimated correctly and delivered quickly? The only place we see that in life is artistic performance. A pianist plays, from memory, a piece that she only decided a few months ago to perform. An hour long, no mistakes. The event starts, and finishes, on time. The customer — the audience — rises to its feet in noisy appreciation. The enabler of perfection is seeking beauty, or doing a thing for its own sake.

Many software developers write software because it’s beautiful, fun, or spiritually rewarding. And that reason engenders the highest-quality work. A quality that delivers on time, with no costly rework. Functionality. Profit too.

Somehow, in the commercial context, that reason why we write software must be harnessed and encouraged by software managers. Otherwise, dismissing a developer’s data with an intuitive estimate is not a fall-back, but falling backwards.

Some return for your U.S. taxpayer dollars:

CrossTalk, The Journal of Defense Software Engineering is an approved Department of Defense journal. CrossTalk’s mission is to encourage the engineering development of software in order to improve the reliability, sustainability, and responsiveness of our warfighting capability and to inform and educate readers on up-to-date policy decisions and new software engineering technologies.

Whatever you may think about “warfighting”, CrossTalk sometimes has some really good articles, and even some great ones. Here are two, from the December 2007 issue:

Very good, about software maintenance:

Geriatric Issues of Aging Software

Great, about moving beyond “escaped defects” to defect prevention: 

Advancing Defect Containment to Quantitative Defect Management

Both are thorough and comprehensive—worth reading!

On Slow Leadership today they’re talking about Occam’s Razor. Choosing the simplest explanation. And the dangers of setting numerical targets.

But metrics are so easy to collect these days. The temptation to count things is great.

Let’s say you are deploying a new process, or tracking progress on an existing one. What harm could there be in counting, looking at the numbers, and making decisions based on them? Isn’t that sound management?

The problem is assuming that by counting something you learn more about it.

Actually all you learn is how many of something there were.

You don’t know anything about those somethings, nor why the count came out as it did.

When you count deployment events, there is no positive integer count that tells you much by itself.

Even zero doesn’t tell you unequivocally what happened. I can think of at least 3 possibilities about the events:

a) Didn’t happen
b) Happened but insubordination or fear among the counters
c) Happened but misunderstanding about what to count

Things get worse if the numbers are positive. How do you know whether a number is large or small? More, or less?

For example, trying to measure software development, let’s say you counted 24 high-severity defects last month. Is that high or low? Is it more or less than the month before? Kind of depends, doesn’t it, on how much code you wrote, how much testing was done, how good the testing was, which defects were recorded, and so on.

Forget distributions — pie charts and the like. How do you know if people understood which category an event belonged in?

What does all this leave us with? No graphs? No counting? No decision-making based on numbers? Back to the Stone Age?

No. It just means something counter-intuitive in today’s modern world of computers: knowledge before counting.

First learn the qualitative details of your events. What is a satisfactory defect entry? A relevant test result? A productive code review? Make sure all participants can recognize them.

Then you can start counting.

1, 2, 3 …

P.S. Want extra credit? Read just the first page of What is Mathematics? by Courant and Robbins. The section is called “The Natural Numbers: Introduction”.