While our son was growing up, though, the idea of spending a summer away from my family really didn’t appeal. He’s now graduated from high school and is off in the world, so this has become a fairly realistic option. Consequently I’m talking to various friends, alums, and industry types in an effort to identify plausible opportunities.

I’m pretty flexible on this, but I’m definitely interested in doing development rather than R&D (the activity that my degrees and experience would tend to suggest). I’d probably prefer to be a member of an agile team rather than working primarily solo on a task. It would also be important that the job really just be a summer gig; my job here is more than enough to keep me busy, so I’m not looking for something that will bleed into the school year.

Anyone out there know of any opportunities that might make sense? Feel free to leave a comment or contact me directly (mcphee AT morris DOT umn DOT edu).

Many thanks!

P.S. In case you stumble here and have no idea who I am, a very brief synopsis: I’ve been programming since the late 70’s, and teaching computer science here at the University of Minnesota, Morris, since 1991. I have lots of experience with Java, also quite a lot of Ruby, Python, and Groovy. I’m useful with JavaScript and Grails, and have experience with Rails but it’s a little rusty. I was once quite competent at C and then C++, but haven’t used either in years. Most of my dissertation work (mid- to late- 80’s) was in Common LISP; more recently I’ve been teaching Scheme and Racket a lot, and also have some experience with Clojure, Erlang, and Haskell. I learn new languages, tools, and systems pretty quickly, as that is often required in my job. My primary research area has been evolutionary computation (EC), and I’ve led or assisted in the design and implementation of numerous EC systems over the years. I’m experienced in experimental design, data collection, and statistical analysis and data visualization. I’m an experienced writer and public speaker; my research is heavily cited and has won several best paper awards.

It’s a true joy, and I’m really looking forward to being able to read the “finished” work. It’s a project oriented book, though, which means that to really learn from it we’re going to have to play along at home, programming, and experimenting, and analyzing. We’re all going to have to set aside some time for this when it does come out. But it’ll be worth it. And while the claim is that we’ll be learning about Generative Projects or Genetic Programming or whatever Bill decides to have GP stand for when he wraps this thing, we’re also going to learn a lot about software development and problem solving along the way, from someone who’s got a lot of great things to say on those important subjects. So we’d best buckle our seatbelts!

I do, however, have to take issue with a claim Bill makes in the introductory “About this book” section:

This is not a textbook. If your instructors try to use it as one, complain. If they argue, send them to me.

To be honest, it’s turning out a lot more like an anti-textbook. It will not improve your performance on tests. We’ll use “advanced” techniques before we discuss “basic” ones, ignore common practices to focus on more appropriate contingent solutions for specific problems, and may not even touch on techniques your instructors think you ought to know.

Remember, my goal is to support and extend your existing skills so you can productively explore further. Seek comprehensive knowledge and historical grounding in other books. They are full of it.

I can’t help but think he had me in mind when he wrote these lines, and I doubt he’ll be surprised to learn that I’m certainly seriously considering using it in my Evolutionary Computation and Artificial Intelligence course in the Spring. <snigger>

The issue, I suspect, is what one considers a “textbook” and what the point of such a thing is. Traditionally textbooks are big, fat things that provide a “coherent” overview of “everything you need to know about subject X”. I’ve got a bunch of these on my shelves with nice overviews of things like calculus and physics and chemistry. Old things, with a clear sense of what matters and what order it should be presented in.

In teaching computer science, however, things are typically much less clear. What’s “vital” in our field? What’s the “natural” or most pedagogically sensible order to present that material? There are lots of ideas out there, but hardly consensus. And that’s on the introductory material; my upper division electives usually focus on material where there’s even less agreement.

Starting in the late 90’s I started moving away from “traditional” textbooks when I could. I found that the most important part of most of my courses was process rather then content. Most computing textbooks I’ve seen are, unfortunately, much stronger on content than process. There are piles of books full of catalogues of algorithms, but they’re mostly presented like magic tricks, things Very Smart People dreamt up in some strange opium vision, inaccessible to the rest of us. The challenge in my experience is coming up with these visions, the process and the problem solving. So a book like Fowler’s wondrous Refactoring is far more educational in the long run, giving us skills for life instead of a box full of baubles.

The contrast between process and content has been made far more pronounced by the web and its many tools and collections. Young folks (and even this old guy) almost never reach for a book when they want to look something up; that’s what Google is for. The reference value of big encyclopedias of “facts” is greatly diminished when we can so easily find things on-line, but a well-written description of an important process is still a treasure.

Which brings us back to Bill’s book.

He claims it’s not a textbook because he doesn’t plan to cover “every” technique or concept or approach. He’s going to doing things in the “wrong” order. Foolishly, he’s not going to help improve our performance on tests, choosing to instead helping us develop skills that will help us for years to come.

Silly, silly man.

So, yeah, I do want to use Bill’s book in my class, and my students are more than welcome to complain to me or take it up with Bill. I doubt, however, that they will. The writing is opinionated and ornery and humble in all the right ways, and the emphasis on projects and problem solving are the sort of thing our students just eat up. I’m guessing that they’d happily line up behind something like this vs. a more traditional text.

As will I.

P.S. You should check out the book’s LeanPub site (https://leanpub.com/pragmaticGP) and sign up for notification. It’s a great way for people to help delude Bill into thinking that he’s not actually wasting his time.

Or something like that.

• Models of Computing Systems
• Fuzzy logic and fuzzy sets
• Refactoring

I’ve taught Refactoring several times and have a pretty good handle on that. Fuzzy Logic I’ve taught once before and am pretty comfortable with. The Systems course, however, is one I’ve never taught before and am still struggling with on a number of levels, including the textbook.

Any suggestions and ideas on any of these would most certainly be appreciated. I’ll say a little more about each course below the fold for those who want all the gory details.

Models of Computing Systems This is one of our three core courses (the others being Algorithms and Computability, and Software Design and Development), is a 5 credit course (4 hours of lecture and a 2 hour lab each week), and is intended to expose students to computing systems using a layer model that includes as major topics

• Assembly language and a quickie overview of basic architecture
• Operating systems basics, with an emphasis on processes, process management, and threading/concurrancey
• Computer networks
• System administration, including the installation, configuration, and management of common tools like web servers

I’m planning to use x86 assembly for the first item, and the sysadmin work will happen on Linux boxes. I’m planning (still in a vague way at the moment) to try to use computer security issues to motivate/illustrate a number of key concepts in this course. Things like file system and disk structure can be pretty abstract, for example, but I’m thinking that doing a lab where we see how those decisions lead to lots of “erased” data being recoverable might make it seem more “real” and significant.

I realize that no book is going to cover all these things, and on-line resources plus lecture can provide the necessary background for several of these. This course has typically using a “standard” OS book like Silberschatz, et al, but this is large and expensive and really only addresses one of the four topics. If anyone knows of a good book that would touch meaningfully on more than one of these areas, though, that would be really helpful.

Fuzzy logic This is a 2 credit elective course, and should provide a reasonable background in the theoretical definitions and concepts in fuzzy logic, as well as giving the students a chance to apply those ideas. (My current plan is to write robot race car drivers using fuzzy notions of concepts like fast, slow, near, and straight.)

I’m probably more interested in solid coverage of the theoretical material than the applications side. The students will benefit from a good introduction and reference on the mathematical material, and I can probably handle the motivation and application side in class pretty well.

When I last taught this (Spring, 2006) I used An Introduction to Fuzzy Logic for Practical Applications by Kazuo Tanaka, translated by T. Niimura. That book actually worked quite decently. There were some predictable translation issues, but nothing that we couldn’t manage.

Refactoring This is also a 2 credit elective course. I’ve taught this course several times, and I’m likely to again use a combination of Fowler’s Refactoring and Kerievsky’s Refactoring to Patterns. Fowler is the “standard” in the field; the first five chapters of his book are absolutely classic material on the realities of software development and should be read by pretty much anyone who aspires to write good software. Kerievsky’s book builds on Fowler and does a really nice job of demystifying design patterns, converting them from magical insights codified by geniuses into things you could discover on your own through careful refactoring. When I last taught it I think I had Kerievsky as the required text and Fowler as the optional book. Given that together they still add up to less than $100, I’m tempted to require them both. We have lots of copies of Fowler in the lab, though, so I might just require Kerievsky again. Ideas/thoughts on this would definitely be appreciated.

One of the problems we’ve run into in this course in the past has been attempting refactorings on code with limited automated testing. Without good tests you lose your courage to refactor mercilessly, or you have false courage and end up breaking things without realizing it until (sometimes much) later. With only 2 credits to work with, however, you don’t want to spend two weeks writing unit tests for a system before you can start refactoring it, especially when you don’t really understand what the units are and what they’re supposed to be doing. This time I’m planning to use BDD tools like Cucumber, RSpec, and JBehave this time to more cheaply write high level acceptance/functional tests that exercise the key parts of the system in meaningful ways without getting bogged down in a bunch of poorly understood unit tests. We’ll see how that goes.

Wrap-up

So, there they be. Any thoughts, ideas, or suggestions would be greatly appreciated, especially on the Models of Computing Systems course.

A tale of mismanagement, overly enthusiastic love of buzz words and the Next Big Thing, and hiring good people and making them do stupid things.

The pig go. Go is to the fountain. The pig put foot. Grunt. Foot in what? ketchup. The dove fly. Fly is in sky. The dove drop something. The something on the pig. The pig disgusting. The pig rattle. Rattle with dove. The dove angry. The pig leave. The dove produce. Produce is chicken wing. With wing bark. No Quack.

Thanks to MJ for the pointer. As he said “makes one weep for our kind”.

One way this has often played out has been in debates over programming languages and development tools. Students are (quite legitimately) concerned with their near term employment prospects, and so they tend to focus what they’ve heard of, and what they see in the job web sites. Unfortunately that is almost always an exercise in looking backwards in time. When I started in 1991, the problem was getting students out of Pascal and C and start thinking about objects. Now we’re working to add things like Ruby and Python to our Java-heavy toolkit. Constant throughout has been the difficult task of getting them to take (semi-) functional languages (Scheme, Haskell) seriously or, in fact, any language doesn’t have a “For dummies” book at their local mega-bookshop.

I need to be fair, though, and make it clear that we’ve always had students who could see the bigger picture, and have often pushed us faculty to open some important new doors. I suspect that we’ve actually been luckier in that respect at UMM than many other programs. That said, you still get groaners (often very vocal) who never seem to be happy unless you’re emphasizing whatever tool or language they’re firmly convinced is their only road to employment.

This is one of the reasons that it makes me so happy to see the list of programming languages used in the One Laptop Per Child project:

We will support five programming environments on the laptop: (1) Python, from which we have built our user interface and our activity model; (2) Javascript for browser-based scripting; (3) Csound, a programmable music and audio environment; (4) Squeak, a version of Smalltalk embedded into a media-rich authoring environment; and (5) Logo. We will also provide some support for Java and Flash.

OK, we can debate the details (and I’m sure people have and will), but let’s skip all that shall we? Let’s instead note that none of these was a “heavy hitter” 5 or 10 years ago, and there are plenty of people who would (wrongly in my opinion) argue that none are terribly important today. How many data structures classes in the U.S., for example, (a key “bread and butter” course in most computing curriculums) use any of these languages? I’m sure there are a few (especially Python), but proportionally I bet it’s pretty tiny. (Try searching either Amazon or the web for textbooks for such a course, for example.)

It’s also worth considering impact here. Sure, I doubt that anyone’s likely to start building inventory control systems in Logo, but should that be the issue? What’s the real opportunity for impact here? How do I change the world? By building accounting systems? Or by contributing to a project that plans to put computers and software in the hands to millions of kids all around the world?

You want to make the world a better place? You want to really fight terrorism? Then give people hope, a chance to grow and make their world better. Give them something to protect. Contribute to a project like this.

And, if you’re contributing to this project, you apparently program in Python, JavaScript, CSound, Squeak and Logo.

So let’s put an end to the whining about these not being “real” programming languages and nobody building “real” programs with them. I’ve written a crapload of Java code in my day that only a handful of people will ever use. Some bright bulbs used Squeak to build Scratch, which I suspect will be used by millions. Hmmm … which do I find more impressive?