My book is live!

I finally got my book effort officially kicked off now.  I'm planning on iterating through chapter development and getting the first early release done in January.  Also kicking off my new community group in January for Austin DIG and the Idea Flow project, a lot to do!

My awesome friend Wiley also helped me design the book cover. :)

http://leanpub.com/ideaflow

Breaking Things That Work

I went to NFJS today, and went to a talk on "Complexity Theory and Software Development" by Tim Berglund.  It was a great presentation, but one idea in particular stuck out to me.  Tim described the concept of "thinking about a problem as a surface."  Imagine yourself surrounded by mountains, everywhere you look - you want to reach as high as you can.  But from where you are, you can only see potential peaks near by. Beyond the clouds and the mountain range obstructing your view, might be the highest peak of all.

With agile and continuous improvement come a concept of tweaking the system toward perfection.  But a tweak always takes me to somewhere nearby - take a step, if it wasn't up, step back to where I was.  But what if my problem is a surface, and the solution is some peak out there I can't see?  Or even if it is a peak I can see...  if I only ever take one small step at a time, I'll never discover that other mountain...

Maybe sometimes we need to leap.  Maybe sometimes we need to break the things that are working just fine.  Maybe we should do exactly what we're not "supposed to do", and see what happens.  

Now imagine a still pool of water... I drop in a stone, and watch the rings of ripples growing outward in response.  I can see the reaction of the system and gain insight into the interaction.  But what if I cast my stone into a raging river? I can certainly see changes in waves, but which waves are in response to my stone?  It seems like I'll likely guess wrong.  Or come to completely wrong conclusions about how the system works.

With all to variance in movement of the system - maybe it takes a big splash to in order to improve our understanding of how it works?  Step away from everything we know and make a leap for a far away peak?

Here's one experiment.  We've noticed that tests we write during development provide a different benefit when you write them vs when they fail and you need to fix them later.  How you interact with them and think about them totally changes.   So maybe the tests you write first and the ones you keep, shouldn't be the same tests?  We started deleting tests.  If we didn't think a test would keep us from making a mistake later, it was gone.   We worked at optimizing the tests we kept for catching mistakes. But this made me wonder about a pretty drastic step - what if you designed all code with TDD, then deleted all your tests? What if you added tests back that were only specifically optimized for the purpose of coming back to later?  If you had a clean slate, and you were positive your tests worked already, what would you slip in the time capsule to communicate to your future self?

What Makes a "Better" Design?

An observation... there are order of magnitude differences in developer productivity, and a big gap in between - like a place where people get stuck and those that make a huge leap. 

Of the people I've observed, it seems like there's a substantial difference in the idea of what makes a better design, as well as an ability to create more options. Those that don't make the leap tend to be bound to a set of operating rules and practices that heavily constrain their thinking. Think about how software practices are taught... I see the focus on behavior-focused "best practices" without thinking tools as something that has stunted the learning and development of our industry. 

Is it possible to learn a mental model such that we can evaluate "better" that doesn't rely on heuristics and best practice tricks? If we have such a model, does it allow us to see more options, connect more ideas? 

This has been my focus with mentoring - to see if I could teach this "model." More specifically a definition of "better" that means optimizing for cognitive flow. But since its not anything static, I've focused on tools of observation. By building awareness of how the design affects that flow, we can learn to optimize it for the humans. 

A "better" software design is one that allows ideas to flow out of the software, and into the software more easily.

Effects of Measuring

As long as measurements are used responsibly, not for performance reviews or the like, it doesn't affect anything, right?


It's not just the measurements being used irresponsibly - the act of measuring effects the system, our understanding, and our actions. Like a metaphor - metrics highlight certain aspects of the system, but likewise hide others. We are less likely to see and understand the influencers of the system that we don't measure... and in software the most important things, the stuff we need to understand better, we can't really put a number on. 

Rather than trying to come up with a measurement, I think we should try and come up with a mental model for understanding software productivity. Once we have an understanding of the system, maybe there is hope for a measurement. Until then, sustaining productivity is left to an invisible mystic art - with the effects of productivity problems being so latent, by the time we make the discovery, its usually way too late and expensive to do much about it. 

Productivity understanding, unlike productivity measuring, I believe is WAY more worth the investment. A good starting point is looking at idea flow.

Humans as Part of the System

I think about every software process diagram that I've ever seen, and every one seems to focus on the work items and how they flow - through requirements, design, implementation, testing and deployment.  Whether short cycles or long, discreet handoffs or a collapsed 'do the work' stage, the work item is the center piece of the flow.

But then over time, something happens.  The work items take longer, defects become more common and the system deteriorates.   We have a nebulous term to bucket these deterioration effects - technical debt.  The design is 'ugly', and making it 'pretty' is sort of a mystic art.   And likewise keeping a software system on the rails is dependent on this mystic art - that seems quite unfortunate.  So why aren't the humans part of our process diagram - if we recognized the underlying system at work, could we learn how to better keep it in check?

What effect does this 'ugly' code really have on us?  How does it change the interactions with the human? What is really happening?

If we start focusing our attention on thinking processes instead of work item processes, how ideas flow instead of how work items flow... the real impact of these problems may actually be visible.  Ideas flow between humans.  Ideas flow from humans to software.  Ideas flow from software to humans.  What are these ideas?  What does this interaction look like?

Mapping this out even for one work item is enlightening.  It highlights our thinking process.  It highlights our cognitive missteps that lead us to make mistakes.  It highlights the effects of technical debt.  And it opens a whole new world of learning.

Addressing the 90% Problem

If I were to try to measure the time that I spent thinking, analyzing and communicating versus actually typing in code, how much of the time would it be? If I were to guess, I'd say something at least 90%.  I wonder what other people would say? Especially without being biased by the other opinions in the room?

We spend so much of our time trying to understand... even putting a dent in improvement would mean -huge- gains in productivity. So...

How can we improve our efficiency at understanding?

How can we avoid misunderstanding, forgetting, or lack of understanding?

How can we improve our ability and efficiency at communicating understanding?

How might we reduce the amount of stuff that we need to understand?

These are the questions that I want to focus on... its where the answers and solutions will make all the difference.

Mistakes in a World of Gradients

I've been working on material for avoiding software mistakes, and have been searching for clarity on how to actually define "mistake."

I've been struggling with common definitions that are very black and white about what is wrong and considered "an error" or "incorrect".  Reality often seems more of a gradient than that, and likewise avoiding mistakes should maybe be more a matter of avoiding poor decisions in favor of better ones?

I like this definition better, because it accounts for the gradient in outcome, without missing the point.

"A human action that produces an incorrect or inadequate result. Note: The fault tolerance discipline distinguishes between the human action (a mistake), its manifestation (a hardware or software fault), the result of the fault (a failure), and the amount by which the result is incorrect (the error)."

 GE Russell & Associates Glossary - http://www.ge-russell.com/ref_swe_glossary_m.htm