Part Two of Chapter Two in a very personal history of programming
In the last article in this series, I wrote about how for the first twenty years of programming history, most programmers in the field, the ones writing the programs that got used in real life, were self-taught. There were no schools for programmers, no formal practices, no body of knowledge, no discipline to speak of. Programmers either learned from a mentor or pursued their own individual instincts and intuitions.
A truly delightful tale illustrating this reality is The Story of Mel, posted to Usenet¹ by Ed Nather in 1983. Mel was a bare metal programmer. In fact the jargon file entry for “bare metal” refers to this very story.
Don’t worry if you do not understand every word or technical detail in the story below, you’ll still enjoy it, and I believe you will get the gist. It is a good-humored tongue-in-cheek response to a somewhat self-serious letter to the editor of Datamation magazine that recycled the Real Men Don’t Eat Quichetrope of the 1980s and was entitled Real Programmers Don’t Use PASCAL² in which the author claims that FORTRAN is the only language a “real programmer” would use. Pascal is a very structured language, perhaps the most structured of them all. I will explain about this right after the story.
A recent article devoted to the *macho* side of programming made the bald and unvarnished statement:
Real Programmers write in Fortran.
Maybe they do now, in this decadent era of Lite beer, hand calculators and “user-friendly” software but back in the Good Old Days, when the term “software” sounded funny and Real Computers were made out of drums and vacuum tubes, Real Programmers wrote in machine code. Not Fortran. Not RATFOR. Not, even, assembly language. Machine Code. Raw, unadorned, inscrutable hexadecimal numbers. Directly.
Lest a whole new generation of programmers grow up in ignorance of this glorious past, I feel duty-bound to describe, as best I can through the generation gap, how a Real Programmer wrote code. I’ll call him Mel, because that was his name.
I first met Mel when I went to work for Royal McBee Computer Corp., a now-defunct subsidiary of the typewriter company. The firm manufactured the LGP-30, a small, cheap (by the standards of the day) drum-memory computer, and had just started to manufacture the RPC-4000, a much-improved, bigger, better, faster — drum-memory computer. Cores cost too much, and weren’t here to stay, anyway. (That’s why you haven’t heard of the company, or the computer.)
I had been hired to write a Fortran compiler for this new marvel and Mel was my guide to its wonders. Mel didn’t approve of compilers.
“If a program can’t rewrite its own code,” he asked, “what good is it?”
Mel had written, in hexadecimal, the most popular computer program the company owned. It ran on the LGP-30 and played blackjack with potential customers at computer shows. Its effect was always dramatic. The LGP-30 booth was packed at every show, and the IBM salesmen stood around talking to each other. Whether or not this actually sold computers was a question we never discussed.
Mel’s job was to re-write the blackjack program for the RPC-4000. (Port? What does that mean?) The new computer had a one-plus-one addressing scheme, in which each machine instruction, in addition to the operation code and the address of the needed operand, had a second address that indicated where, on the revolving drum, the next instruction was located. In modern parlance, every single instruction was followed by a GO TO! Put *that* in Pascal’s pipe and smoke it.
Mel loved the RPC-4000 because he could optimize his code: that is, locate instructions on the drum so that just as one finished its job, the next would be just arriving at the “read head” and available for immediate execution. There was a program to do that job, an “optimizing assembler”, but Mel refused to use it.
“You never know where it’s going to put things”, he explained, “so you’d have to use separate constants”.
It was a long time before I understood that remark. Since Mel knew the numerical value of every operation code, and assigned his own drum addresses, every instruction he wrote could also be considered a numerical constant. He could pick up an earlier “add” instruction, say, and multiply by it, if it had the right numeric value. His code was not easy for someone else to modify.
I compared Mel’s hand-optimized programs with the same code massaged by the optimizing assembler program, and Mel’s always ran faster. That was because the “top-down” method of program design hadn’t been invented yet, and Mel wouldn’t have used it anyway. He wrote the innermost parts of his program loops first, so they would get first choice of the optimum address locations on the drum. The optimizing assembler wasn’t smart enough to do it that way.
Mel never wrote time-delay loops, either, even when the balky Flexowriter required a delay between output characters to work right. He just located instructions on the drum so each successive one was just *past* the read head when it was needed; the drum had to execute another complete revolution to find the next instruction. He coined an unforgettable term for this procedure. Although “optimum” is an absolute term, like “unique”, it became common verbal practice to make it relative: “not quite optimum” or “less optimum” or “not very optimum”. Mel called the maximum time-delay locations the “most pessimum”.
After he finished the blackjack program and got it to run, (“Even the initializer is optimized”, he said proudly) he got a Change Request from the sales department. The program used an elegant (optimized) random number generator to shuffle the “cards” and deal from the “deck”, and some of the salesmen felt it was too fair, since sometimes the customers lost. They wanted Mel to modify the program so, at the setting of a sense switch on the console, they could change the odds and let the customer win.
Mel balked. He felt this was patently dishonest, which it was, and that it impinged on his personal integrity as a programmer, which it did, so he refused to do it. The Head Salesman talked to Mel, as did the Big Boss and, at the boss’s urging, a few Fellow Programmers. Mel finally gave in and wrote the code, but he got the test backwards, and, when the sense switch was turned on, the program would cheat, winning every time. Mel was delighted with this, claiming his subconscious was uncontrollably ethical, and adamantly refused to fix it.
After Mel had left the company for greener pa$ture$, the Big Boss asked me to look at the code and see if I could find the test and reverse it. Somewhat reluctantly, I agreed to look. Tracking Mel’s code was a real adventure.
I have often felt that programming is an art form, whose real value can only be appreciated by another versed in the same arcane art; there are lovely gems and brilliant coups hidden from human view and admiration, sometimes forever, by the very nature of the process. You can learn a lot about an individual just by reading through his code, even in hexadecimal. Mel was, I think, an unsung genius.
Perhaps my greatest shock came when I found an innocent loop that had no test in it. No test. *None*. Common sense said it had to be a closed loop, where the program would circle, forever, endlessly. Program control passed right through it, however, and safely out the other side. It took me two weeks to figure it out.
The RPC-4000 computer had a really modern facility called an index register. It allowed the programmer to write a program loop that used an indexed instruction inside; each time through, the number in the index register was added to the address of that instruction, so it would refer to the next datum in a series. He had only to increment the index register each time through. Mel never used it.
Instead, he would pull the instruction into a machine register, add one to its address, and store it back. He would then execute the modified instruction right from the register. The loop was written so this additional execution time was taken into account — just as this instruction finished, the next one was right under the drum’s read head, ready to go. But the loop had no test in it.
The vital clue came when I noticed the index register bit, the bit that lay between the address and the operation code in the instruction word, was turned on — yet Mel never used the index register, leaving it zero all the time. When the light went on it nearly blinded me.
He had located the data he was working on near the top of memory — the largest locations the instructions could address — so, after the last datum was handled, incrementing the instruction address would make it overflow. The carry would add one to the operation code, changing it to the next one in the instruction set: a jump instruction. Sure enough, the next program instruction was in address location zero, and the program went happily on its way.
I haven’t kept in touch with Mel, so I don’t know if he ever gave in to the flood of change that has washed over programming techniques since those long-gone days. I like to think he didn’t. In any event, I was impressed enough that I quit looking for the offending test, telling the Big Boss I couldn’t find it. He didn’t seem surprised.
When I left the company, the blackjack program would still cheat if you turned on the right sense switch, and I think that’s how it should be. I didn’t feel comfortable hacking up the code of a Real Programmer.
As the story so wonderfully illustrates, the first few generations of programmers were quite accustomed to doing as they wished, using idiosyncratic methods and highly personal styles of programming. And there was sometimes resentment among them towards the academics and their efforts to promote structured programming.
For the benefit of readers who are not programmers, the concept of structured programming was closely tied to programming languages, so one speaks of a “structured programming language” or sometimes simply a “structured language”. In an unstructured language, the format of the code will not give us any clues about the flow of control (the order in which statements or instructions are executed). Here is what that looks like written in pseudo code(a description of a program that is not written in any specific computer language):
1 START PROGRAM
2 GET list_of_names from user
3 COUNT = number of items in list_of_names
4 READ first item from list_of_names
5 DO thing a
6 DO thing b
7 IF result of thing b is TRUE GOTO line 11
8 DO thing c
9 DO thing d
10 END IF
11 DELETE first item from list_of_names
12 SUBTRACT 1 from COUNT
13 IF COUNT = 0
15 END IF
16 GOTO LINE 4
17 END IF
18 END PROGRAM
This code will loop as long as there are names in the list to process, and it will jump over lines 8 & 9 if the result of line 7 “evaluates” as TRUE. Just by glancing at it there is nothing in the structure of this code to tell you that. You have to read it line by line to know. And even then, there is no clue to tell you that lines 8 & 9 are special cases and that the “result of thing b” is usually TRUE.
A “structured language” would be one that did not provide a GOTO instruction (lines 7 & 16 above) and instead provided higher level concepts such as WHILE and FUNCTION, as in the example below:
GET list_of_names from user
WHILE list_of_names is not empty
READ first item from list_of_names
DO thing a
DO thing b
IF result of thing b is TRUE
DELETE first item from list_of_names
DO thing c
DO thing d
These two examples do the same thing(s); however, proponents of structured programming argue the second one is more readable, less error prone, and easier/faster to write, therefore enhancing productivity. Readability in particular, matters because as it happens, programmers spend quite a bit more time reading already written code (even when it is their own) then they do writing it.³
During the sixties, Dijkstra published seven papers. In a 1996 poll of over a thousand professors of computer science, four of those papers were selected as being among the thirty-eight most influential papers on computer science ever written. But by far his most recognizable contribution was a short five-page letter in defense of structured programming sent in 1968 to the editor of the Journal of The Association for Computing Machinery, the leading publication in computer science at the time. Dijkstra sent the letter with the undramatic title: A Case Against the Goto Statement, but editor Niklaus Wirth (who created Pascal in 1970) somewhat mischievously changed the title, using a popular journalistic cliché of the times, to Go To Statement Considered Harmful.
This letter to the editor triggered at least two decades of debate and remains (probably) the most recognizable (yet possibly least read) computer science article of all time. One does not have to look far to find contemporary citations and discussion. The publication was significant in many ways, not least of which was that it triggered one of the earliest (perhaps the first) of the Holy Wars⁴ or Religious Wars of which Real Programmers Don’t USE PASCAL and The Story of Mel are just small examples. From 1970 onwards advances in programming would be punctuated by rabid advocacy and totalitarian claims of supremacy for competing conceptual models.
Programming, perhaps more so than any other applied science, inspires a fanatical quest for perfection and purity. Fred Brooks suggests that it might be due to the fact that “The programmer, like the poet, works only slightly removed from pure thought-stuff. He builds his castles in the air, from air, creating by exertion of the imagination. Few media of creation are so flexible, so easy to polish and rework, so readily capable of realizing grand conceptual structures”.⁹
An article from Business Insider in 2015⁵ offers this somewhat less romantic explanation of “Why coders get into ‘religious wars’ over programming languages”, saying, “… every programming language represents a philosophy as much as it does a product”.
Since Go To Statement Considered Harmful was first published there has been no lack of reasons to start a war. Prior to its publication only 200 languages had been created, but in the time since, a few thousand more came into being.
There are many important languages that I will not write about in this series of articles, because I have a particular focus on Enterprise IT and IT project failure. For example, I love the Python and Ruby languages, but they do not play a significant role in corporate IT and project failure. You will instead find Python being used by researchers for data mining and artificial intelligence. Also, both Python and Ruby are often found in Internet companies like Google, Dropbox, or Uber and in startup companies galore.
Illumination and marginalia
I think of the first two decades after 1949 as the dark ages of programming. Not many historical documents remain from this time for programming. Historical documentation abounds for the computers themselves. As I wrote earlier, hardware engineering was highly respected and recognized. The profession of electrical engineering was well organized, well documented, and acknowledged as being very significant. Yet the programmers labored in obscurity.
The typical programmer was self-taught and highly internally motivated. Apart from a small number of young prodigies, a programmer was more likely than not already highly educated, often with a Masters or PhD which was how they came to be anywhere near a computer to begin with. They tended to be a very smart bunch.
There were no schools or courses for programmers, and when computers cost millions of dollars there were no casual programmers. They were some of the brightest lights of humanity simply by virtue of how difficult it was to become a programmer at that time.
For every Dijkstra or Knuth or Wirth that we know about, there were a thousand Mels, creating works of pure genius, of elegance and rare beauty, that were to be forever lost as the magnetic tapes and punch cards that preserved the deepest thoughts of this hidden generation became obsolete and were binned, unceremoniously and out of sight.“The trouble with programmers is that you can never tell what a programmer is doing until it’s too late.”
~ Seymour Cray
Inventor of the Cray supercomputer
 Martin, Robert C., and Lei Han. Clean code. Publishing House of Electronics Industry, 2012
 Brooks, Frederick P. The Mythical Man-Month And Other Essays On Software Engineering. Chapel Hill, Dept. Of Computer Science, University Of North Carolina At Chapel Hill, 1974, p.21
This article is an excerpt from my upcoming book The Chaos Factory which explains why most companies and government can’t write software that “just works”, and how it can be fixed.