Tuesday, February 13, 2018

My most insidious bug

I was asked, "As a coder, what is the most insidious bug you have ever come across, and how did you find it?"

It’s really hard to pick one error out of hundreds I’ve encountered in my long career, but some of the toughest have been:

  • compiler errors, where the compiler has created object code incorrectly. We usually found these by hacking around, changing the source code to express the program in different ways, or by examining the object code the compiler had produced.
  • hardware errors, both from the failure of a component and an actual design error in the hardware. Such errors are not as frequent today as they were in the old days of vacuum tubes (or relays), but in a way that infrequency makes them all the more difficult when they do occur, because we have so little experience with them.
  • requirements errors, where the program has actually solved the wrong problem. These errors can usually be found only after users have been in contact with the code for some time, and only when there is some communication channel between the users and the programmers.
  • So, what were your most insidious errors?

You can read more about errors and their consequences in

Errors: Bugs, Boo-Boos, and Blunders (https://leanpub.com/errors)

Saturday, January 06, 2018

New: #System #Design #Heuristics

You'd think that after publishing books for half a century, I'd know how to write a book. If that's what you think, you'd be wrong.

Sure, I've even written a book on writing books (Weinberg on Writing, the Fieldstone Method), and I've applied those methods to dozens of successful books. But way back around 1960, I started collecting notes on the process of design, thinking I would shortly gather them into a book. Back then, I didn't call these bits and pieces "fieldstones," but that's what they turned out to be: the pieces that, when assembled properly, would ultimately become my design book.

Ultimately? Assembled properly? Aye, there's the rub!

Building walls from randomly found fieldstones requires patience. So does writing books by the Fieldstone Method. My Introduction to General Systems Thinking took fourteen years to write. But a writer only lives one lifetime, so there's a limit to patience. I'm growing old, and I'm beginning to think that fifty years is as close to "ultimately" as I'm going to get.

So, I've begun to tackle the task of properly assembling my collection of design fieldstones. Unfortunately, it's a much larger collection that I'd ever tackled before. My Mac tells me I have more than 36,000,000 digitized bytes of notes. My filing cabinets told me I had more than twenty-five pounds of paper notes, but I've managed to digitize some of them and discard others, so there's only a bit more than ten pounds left to consider.

For the past couple of years, I've periodically perused these fieldstones and tried to assemble them "properly." I just can't seem to do it. I'm stuck.

Some writers would say I am suffering from "writer's block," but I believe "writer's block" is a myth. I've published three other books in these frustrating years, so I can't be "blocked" as a writer, but just over this specific design book. You can hear me talk more about the Writer's Block myth on YouTube 


but the short version is that "blocking" is simply a lack of ideas about how to write. I finally decided to take my own advice and conjure up some new ideas about how to write this design book.

Why I Was Stuck

To properly assemble a fieldstone pile, I always need an "organizing principle." For instance, my recent book, Do You Want to Be a (Better) Manager? is organized around the principle of better management. Or, for my book, Errors, the principle is actually the title.  So, I had been thinking the organizing principle for a book on design ought to be Design

Well, that seemed simple enough, but there was a problem. Everybody seemed to know what design is, but nobody seemed able to give a clear, consistent definition that covered all my notes. I finally came to the conclusion that's because "design" is not one thing, but many, many different things.

In the past, I ran a forum (SHAPE: Software as a Human Activity Practiced Effectively) whose members were among the most skilled software professionals in the world. We held a number of threads on the subject, "What is Design?" The result was several hundred pages of brilliant thoughts about design, all of which were correct in some context. But many of them were contradictory.

Some said design was a bottom-up process, but others asserted it was top-down. Still others talked about some kind of sideways process, and there were several of these. Some argued for an intuitive process, but others laid out an algorithmic, step-by-step process. There were many other variations: designs as imagined (intentional designs), designs as implemented, and designs as evolved in the world. All in all, there were simply too many organizing principles—certainly too many to compress into a title, let alone organize an entire book.

After two years of fumbling, I finally came up with an idea that couldn't have been implemented fifty years ago: the book will be composed of a variety of those consulting ideas that have been most helpful to my clients' designers. I will make no attempt (or very little) to organize them, but release them incrementally in an ever-growing ebook titled Design Heuristics.

How to Buy System Design Heuristics

My plan for offering the book is actually an old one, using a new technology. More than a century ago Charles Dickens released many of his immortal novels one chapter at a time in the weekly newspaper. Today, using the internet, I will release System Design Heuristics a single element at a time to subscribing readers.

To subscribe to the book, including all future additions, a reader will make a one-time payment. The price will be quite low when the collection is small, but will grow as the collection grows. That way, early subscribers will receive a bargain in compensation for the risk of an unknown future. Hopefully, however, even the small first collection will be worth the price. (If not, there will be a full money-back guarantee.)

Good designs tend to have unexpected benefits. When I first thought of this design, I didn't realize that it would allow readers to contribute ideas that I might incorporate in each new release. Now I aware of that potential benefit, and look forward to it.

Before I upload the first increment of System Design Heuristics, I'll wait a short while for feedback on this idea from my readers. If you'd like to tell me something about the plan, email me or write a comment on this blog.

Thanks for listening. Tell me what you think.

Sunday, December 31, 2017

What is Software?

Ir's a new year, so let's start out with something fundamental, cleaning up something that's bothered me for many years.

The other day I was lunching with a computer-naive friend who asked, "What is software?"

Seems like it would be an easy question for those of us who make and break software for a living, but I had to think carefully to come up with an explanation that she could understand:

Software is that part of a computer system that adapts the machinery to various different uses. For instance, with the same computer, but different software, you could play a game, compute your taxes, write a letter or a book, or obtain answers to your questions about dating.

I then explained to her that it’s unfortunate that early in the history of computers this function was given the name “software,” in contrast to “hardware.” What it should have been called was “flexibleware.”

Unfortunately the term “soft” has been interpreted by many to mean “easy,” which is exactly wrong. Don't be fooled. 
What we call “hardware” should have been called “easyware,” and what we call “software” could then have been appropriately called “difficultware.”

Monday, December 25, 2017

Unnecessary Code

We were asked, "How can I tell if my code does extra unnecessary work?"
To answer this question well, I’d need to know what you mean by “unnecessary.” Not knowing your meaning, I’ll just mention one kind of code I would consider unnecessary: code that makes your program run slower than necessary but can be replaced with faster code.

To rid your program of such unnecessary code, start by timing the program’s operations. If it’s fast enough, then you’re done. You have no unnecessary code of this type.

If it’s not fast enough, then you’ll want to run a profiler that shows where the time is being spent. Then you search those areas (there can be only one that consumes more than half the time) and work it over, looking first at the design.

There’s one situation I’ve encountered where this approach can bring you trouble. Code that’s fast enough with some sets of data may be unreasonably slow with other sets. The most frequent case of this kind is when the algorithm’s time grows non-linearly with the size of the data. To prevent this kind of unnecessary code, you must do your performance testing with (possibly artificially) large data sets.

Paradoxically, though, some algorithms are faster with large data sets than small ones.

Here’s a striking example: My wife, Dani, wanted to generate tests in her large Anthropology class. She wanted to give all students the same test, but she wanted the questions for each student to be given in a random order, to prevent cheating by peeking. She gave 20 questions to a programmer who said he already had a program that would do that job. The program, however, seemed to fall into an unending loop. Closer examination eventually showed that it wasn't an infinite loop, but would have finally ended about the same time the Sun ran out of hydrogen to burn.

Here’s what happened: The program was originally built to select random test questions from a large (500+ questions) data base. The algorithm would construct a test candidate by choosing, say, twenty questions at random, then checking the twenty to see if there were any duplicates among those chosen. If there were duplicates, the program would discard that test candidate and construct another.

With a 500 question data base, there was very little chance that twenty questions chosen at random would contain a duplicate. It could happen, but throwing out a few test candidates didn’t materially affect performance. But, when the data base had only twenty questions, and all Dani wanted was to randomize the order of the questions, the situation was quite different.

Choosing twenty from twenty at random (with replacement) was VERY likely to produce duplicates, so virtually every candidate was discarded, but the program just ground away, trying to find that rare set of twenty without duplicates.

As an exercise, you might want to figure out the probability of a non-duplicate set of twenty. Indeed, that’s an outstanding way to eliminate unnecessary code: by analyzing your algorithm before coding it.

Over the years, I’ve seen many other things you might consider unnecessary, but which do no harm except to the reputation of the programmer. For example:
* Setting a value that’s already set.
* Sorting a table that’s already sorted.
* Testing a variable that can have only one value.

These redundancies are found by reading the program, and may be important for another reason besides performance. Such idiotic pieces of code may be indications that the code was written carelessly, or perhaps modified by someone without full understanding. In such cases, there’s quite likely to be an error nearby, so don’t just ignore them.

Wednesday, December 20, 2017

Which code is more readable?

We were asked, "Which code is more readable, one that uses longer variable names or short ones?" 

Maybe some historical perspective will help answer this question.

In the very early days of computing (I was there), we used short variable names because:

* Programs were fairly short and simple, so scope wasn’t much of a problem.

  • Memories were small, so programmers didn’t want to waste memory with long names.

  • Compilers and assemblers were slow, and long names made them slower.

  • Many compilers and assemblers wouldn’t allow names longer than a few characters, because of speed and memory limitations.

  • We didn’t think much, if at all, about who would maintain a program once it left the hands of the original programmer.

As programs grew larger, one result of short naming was difficult maintenance, so the movement toward longer names grew stronger. It wasn’t helped by COBOL, which asserted that executives should be able to read code. Lots of COBOL code was littered with super-long names, but that didn't help executives read it.

The COBOL argument proved to be nonsense. Still, the maintenance argument for longer, more descriptive names made sense.

Unfortunately, like many movements, the long-name movement went too far, at least for my taste. It wasn’t because long names were harder to write. After all, a typical program is written oncem but read for modification and testing many, many times. So, if long names really made reading easier and more reliable, it was good.

But the length of a name is not really the issue. I’ve seen many programs with long, long names that were so similar that they were easily confused, one with another. For instance, we once wasted many days trying to find an error when the name radar_data_station_#46395_azimuth_reading was mistaken for radar_data_station_#46895_azimuth_reading. Psychologists and writers know well that items in the middle of long lists are frequently glossed over.

So, like lots of other things in software development, long versus short names becomes a tradeoff, a design decision for a programmer for which there is no “right” answer. Programmers must design their name-sets with the same kind of engineering thought they put into all their design decisions.

And, as maintainers modify a program, they must maintain the name-set, so as to avoid building up design debt as the program ages.

So, sorry, there’s no easy answer to this question, nothing a programmer can apply  mindlessly. Just as it’s always been, programmers who think will do a better job than those who blindly follow simplistic rules.

Saturday, December 16, 2017

My First Week in a Software Job

We were asked, "What was your first week like at your first software engineering job?"

In June, 1955, I went to work for IBM in San Francisco. Of course, at that time there was no such thing as "software engineering." In fact, there was no such thing as a "programmer." My title was "Applied Science Representative." I was supposed to apply science to the sale of IBM computers.

I was told that in two weeks I was to teach a course in programming the IBM 650.

That presented a few problems.

  • I had never programmed any computer before.

  • Nobody in the IBM office had ever programmed a computer before.

  • Nobody in the IBM office had ever seen a computer before.

  • There was no computer in the office—just a bunch of punch card machines.

  • In fact, as far as we knew, there was no computer in San Francisco.

I spent the next two weeks in a closet in the IBM office studying all the IBM manuals that were stored there, preparing myself to teach this course. I was pretty much a lone ranger, without the horse or any faithful Indian companion. Actually, no companion at all.

That was over 60 years ago, and now I have a multitude of companions. Even so, it was a special time and an unforgettable first two weeks, so thank you for asking this question.

If you want to know more about what it was like in those thrilling days of yesteryear, you should follow Danny Faught's blog. Back then, we used to listen to the Lone Ranger on radio (there wasn't much, if any, television).

"Hi-Yo, Silver! A fiery horse with the speed of light, a cloud of dust and a hearty ‘Hi-Yo Silver'... The Lone Ranger! With his faithful Indian companion, Tonto, the daring and resourceful masked rider of the plains led the fight for law and order in the early Western United States. Nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoof-beats of the great horse Silver. The Lone Ranger rides again!"

<http://www.geraldmweinberg.com (Formerly The Lone Programmer)

Sunday, December 10, 2017

Do programmers really know how to program?

I was asked, "Do programmers really know how to program?"

I believe this question is unproductive and  vague. What does it mean by “program”?

The person who asked this question seemed to think programmers were not really programming when all they did was copy some existing program, using it whole or perhaps pasting it in as part of a shell.

To me, programming a computer means instructing it to do something you want done, and to continue doing it as desired.

If that’s what we’re asking about, then yes, of course, some of us out here know how to program. (Some do not, of course.)

It is irrelevant how we do that. Whether we use genetic algorithms, cut-and-paste, or divine inspiration? Do we use Scrum or Agile or Waterfall? How about the programming language? C++, or Java, or Lisp, or Python, or APL? Well, none of those choices matters.

Then what does matter? How about, "Can we satisfy someone’s desires?" In other words, can we provide something that someone wants enough to pay what it costs, in time or money? That’s what counts, and we certainly know how do that—sometimes.

Sure, we fail at times, and probably too often. But no profession succeeds in satisfying its customers all the time. Did your teachers always succeed in teaching you something you wanted to know? Do surgeons know how to do surgery?

So what about using existing programs? To my mind, the first and foremost job of a programmer is knowing when not to write a program at all—either because the needed program already exists or because no program was needed in the first place.

In other words, not writing a program when no program is needed is the highest form of programming, and one of the marks of a true expert.

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