Dr. K’s Software Ruminations

Commuter recommendation: Turn off the classic rock. Now! You can only listen to “La Grange” by ZZ Top so many times before you realize that you’re wasting your drive time every single morning and every afternoon. If you’re commuting 20 minutes like me, it’s a modest waste. If you’re living somewhere more metropolitan, the cost is far worse.

What, pray tell, do you do with the time if not drum on the steering wheel or air guitar on the emergency brake? You equip yourself with some MP3 capable sound transmission device (iPod, iPhone, Zune, doesn’t matter) and start looking for meaningful podcasts to energize your mind and soul during an otherwise monotonous daily commute.

First recommendation: CBC Radio has a regular broadcast program called “Ideas” in which they explore a stunningly broad area of topics. Regular broadcast reception is limited to Canada and the northern U.S. But courtesy of podcasting, you can enjoy all these programs at your leisure.

A year or so ago CBC ran a 24-part series entitled, “Ideas: How to Think About Science.” Great material, and very thought-provoking. From Episode 1 (”Leviathan and the Air Pump”) to Episode 24 (”From Knowledge to Wisdom”) this series presents a fresh perspective on science, research, and the nature of what we consider truth and knowledge. Whether you agree with every point made or every interviewed guest, the program is bound to cause you to examine the way you think about the world.

My preferred access is via iTunes subscription with content synchronized to my iPhone. Total running time is about 24 hours, which took me approximately two months to work through during my modest commute and occasional pedestrian meandering. (For those of you in the Bay Area, you should be able to bang this out in about 3 commuter days…

Enjoy!

My most recent post to the PPIG mailing list…

On Jul 4, 2007, at 10:55 AM, Frank Wales wrote:

Charles Knutson wrote:
I believe there is a taxonomy of four types of people, relative to
professional software construction:
1) Those born to code, who need almost no coaching;
2) Those born capable but in need of training in order to be successful;
3) Those not really born to it, but who can be trained sufficiently to make a living;
4) Those whose brains are really not wired to build software at all.

As a matter of interest, do you have a sense of how the general population
is distributed across these suggested types?

Rather than just categories or types, I’d actually propose a spectrum
of capabilities, with people who just ‘get’ computer technology at one
end, people who would sooner eat lint that use it to check their C
programs at the other, and the substantial mass of people bulging
somewhere along the middle. (Of the spectrum.)

I’d speculate, completely without anything beyond years of experience
and arrogant presumption, that the “just get computer technology” end
of the spectrum glows with about 5% of the population, the “couldn’t
write ‘hello, world!’ despite years of practice” end is dimmed by maybe
15% of the population, while the remaining 80% of the population huddles
around various quantumly-distributed embers in between.

I think the end groups are inherent, while the huddles are plastic.

Of course I have no objective evidence for what the actual distribution might be. If anyone has seen any empirical take on this distribution, it would be very interesting. I could easily buy the idea that the top is 5% and the bottom 15%. I also concur completely with the idea of a spectrum. My taxonomy was more of a highly granular way of describing such a spectrum. My apologies to anyone who was expecting a fist fight

Two last thoughts on inherent aptitude:

1) I’ve recently run into students who took the first semester programming class at BYU, did well, got their ‘A’, and then washed their hands of the field and went off to study business or something else. Clearly they are capable of programming, at least at the entry level, but the biggest dissuading factor to them was the inherent motivation (or lack thereof). One of them told me that after spending 15 hours on a programming project, and finally getting it working and passed off, they felt like they had been robbed of 15 hours of their life that they would never get back! That was such a news flash to me! In *my* experience, every program or project I ever finished has carried with it some sense of euphoria and satisfaction that seemed to make all the pain worthwhile. And much of my programming time has been somewhat rhapsodic, like a runner’s high. So while I don’t consider myself to be the most gifted programmer (or even necessarily a member of the first group) I have always been quite satisfied and happy with the process of constructing software.

2) I just started reading Stephen King’s “On Writing.” Interesting to find this quote on page 4 (substitute “computer programmers” for “writers”):

“This is not an autobiography. It is, rather, a kind of curriculum vitae — my attempt to show how one writer was formed. Not how one writer was *made*; I don’t believe writers *can* be made, either by circumstances or self-will (although I did believe those things once). The equipment comes with the original package. Yet it is by no means unusual equipment; I believe large numbers of people have at least some talent as writers and storytellers, and that those talents can be strengthened and sharpened. If I didn’t believe that, writing a book like this would be a waste of time.”

Just a bit more grist for the mill.
Chuck

The following is an email I just posted to the PPIG mailing list, in response to the following comment:

For the record, I believe anyone can learn to program at a professional level. The question is, are they willing to put in the time to acquire all the chunks needed to be an expert? Unfortunately, we can’t force our students to put in the time.

I’m not convinced that absolutely *anyone* can learn to be a professional X (whatever X is). I think there are some who are really just wired to do other things. But I am confident that there are varying degrees to which inherent aptitude plays a role, and similarly varying degrees to which effective learning experiences contribute to facilitate those individuals who can, in fact, be successful at profession X.

As evidence, I offer the following non-empirical anecdote. I started programming in 1973, when I was 13 years old. Our high school had a timesharing account on a mainframe at the University of Northern Iowa, and a DecWriter with a suction cup modem and a rotary phone with a dedicated line to the university. About a half dozen of us math geeks gathered daily in a room to play with the computer (which largely consisted of playing Dungeons and Dragons and Star Trek, with intermittent fits of attempted software design and code construction). Several of my friends just seemed to have the knack right out of the chute. We’d dumpster dive at the university for discarded manuals, and that was all Brian and Doug needed to build software. I tried desperately but couldn’t get it beyond a fundamental level. The other guys were more or less like me, in love with the technology, but not fluent with the incantations.

Years later, in my second semester at the University of Iowa, I had a really well constructed and well presented Computer Science class that focused primarily on design. During that semester, the light came on, and I got it! From that semester it was simply a matter of learning new skills and piling them onto the foundation I had now acquired. I had a very successful professional career building software (HP, Novell, various small companies and consulting gigs), picked a few graduate degrees along the way, and then retired to the university to stop producing and begin pontificating.

As an epilogue, of the group of math geeks that gathered together daily in high school to play with the DecWriter, all but one of us acquired degrees in Computer Science, with the other one (Brian) doing Electrical Engineering.

My personal experience is that I was always fascinated, I was obviously capable, but I needed someone to throw the switch for me to understand how to become self-sustaining after that.

I believe there is a taxonomy of four types of people, relative to professional software construction: 1) Those born to code, who need almost no coaching; 2) Those born capable but in need of training in order to be successful; 3) Those not really born to it, but who can be trained sufficiently to make a living; 4) Those whose brains are really not wired to build software at all.

Just my two cents. Your mileage may vary.
Chuck

One of my all-time favorite a cappella groups, The Bobs, did a brilliant song in 1989 called “Naming the Band” which includes the following lyrics:

“We’re lookin’ for a drummer
Or someone with a van
Our hair is getting longer
But the most important thing is namin’ the band
Namin’ the band.”

It totally captures the dilemma of naming inanimate objects like bands and labs. With my recent research transition from wirelessness to software engineering, we’ve been going through the pain. The old entity was the “Mobile Computing Lab.” Pretty clean, reasonably catchy, only 862 Google hits — and the first hit is us at BYU!

But who wants to be the “Software Engineering Lab”?! Apart from being polysyllabic and terribly boring and generic, it generates 48,500 Google hits. Who can throw their support behind something that vanilla?! Besides, the TLA (”three-letter acronym”) is SEL. It begs the question, “At what price?!” You could refine it to “Software Engineering Research Lab,” which adds two syllables, generates a slightly silly ETLA (”extended three-letter acronym,” a.k.a., “four-letter acronym”), SERL, and more than 2,000 Google hits. So far no good.

We toyed with “Software Engineering Research Group,” which is also polysyllabic, generates 45,900 Google hits, and sports an acronym (SERG) that suggests a sycophantic relationship with one of the Google co-founders. No good.

“We should be writing tunes
and learning where to stand
Instead we’re spending all our time
Doing nothing but … naming the band”

er… lab…

Refusing to accept long-winded mediocrity, we struggle tremendously with naming the lab. We went through “Software Quality Research Lab” (SQRL, pronounced “squirrel”), and the extended version, “Software Quality Research Lab Big Basic Questions” (SQRL BBQ — draw your own conclusions).

“We were gonna call ourselves Elvis Hitler
But someone beat us to the punch”

We had an inspired idea to call it LASER (“Laboratory for Advanced Software Engineering Research”) until we realized that Lori Clarke and Lee Osterweil at UMass Amherst had already stolen our idea. We then toyed with settling for “Laboratory for *Ordinary* Software Engineering Research” (LOSER). Despite its draw, we rejected it for obvious reasons.

“We’ve got our own equipment
and a great rehearsal space
All we need’s a heavy name
to throw in your face”

Like Archimedes, I had my “Eureka!” moment in the tub while struggling in desperation, scribbling ideas on a partially soaked notepad. Unlike Archimedes, I did not consequently run through the streets of Syracuse (or Salem for that matter) naked. Best for everyone involved really.

Okay. Here we go… (Cue the drummer…)

The Sequoia Lab. SEQUOIA — Software Engineering Quality: Observation, Insight, Analysis.

Everyone in the lab immediately jumped on board. Unanimous consent. One explanation is that the idea was brilliant. Another is that the lab members were sick of namin’ the lab and would have agreed to just about anything I threw myself behind. Another is that a rumor had begun to circulate that I was seriously considering going back to SQRL BBQ.

For the record, “Sequoia Lab” generates only 282 Google hits. Also for the record, all of those labs involve forestry (go figure). Not a single software hit. Looks like it’s ours for better or worse. If we begin to be pestered by the spotted owl people, we can always talk to Mr. Brin for potential lab sponsorship and a convenient name change.