Exercises in Programming Style: the python way

Derek Jones from The Shape of Code

Exercises in Programming Style by Cristina Lopes is an interesting little book.

The books I have previously read on programming style pick a language, and then write various programs in that language using different styles, idioms, or just following quirky rules, e.g., no explicit loops, must use sets, etc. “Algorithms in Snobol 4” by James F. Gimpel is a fascinating read, but something of an acquired taste.

EPS does pick a language, Python, but the bulk of the book is really a series of example programs illustrating a language feature/concept that is central to a particular kind of language, e.g., continuation-passing style, publish-subscribe architecture, and reflection. All the programs implement the same problem: counting the number of occurrences of each word in a text file (Jane Austin’s Pride and Prejudice is used).

The 33 chapters are each about six or seven pages long, and contain a page or two or code. Everything is very succinct, and does a good job of illustrating one main idea.

While the first example does not ring true, things quickly pick up and there are lots of interesting insights to be had. The first example is based on limited storage (1,024 bytes), and just does not make efficient use of the available bits (e.g., upper case letters can be represented using 5-bits, leaving three unused bits or 37% of available storage; a developer limited to 1K would not waste such a large amount of storage).

Solving the same problem in each example removes the overhead of having to learn what is essentially housekeeping material. It also makes it easy to compare the solutions created using different ideas. The downside is that there is not always a good fit between the idea being illustrated and the problem being solved.

There is one major omission. Unstructured programming; back in the day it was just called programming, but then structured programming came along, and want went before was called unstructured. Structured programming allowed a conditional statement to apply to multiple statements, an obviously simple idea once somebody tells you.

When an if-statement can only be followed by a single statement, that statement has to be a goto; an if/else is implemented as (using Fortran, I wrote lots of code like this during my first few years of programming):

      IF (I .EQ. J)
      GOTO 100
      Z=1
      GOTO 200
100   Z=2
200

Based on the EPS code in chapter 3, Monolithic, an unstructured Python example might look like (if Python supported goto):

for line in open(sys.argv[1]):
    start_char = None
    i = 0
    for c in line:
        if start_char != None:
           goto L0100
        if not c.isalnum():
           goto L0300
        # We found the start of a word
        start_char = i
        goto L0300
        L0100:
        if c.isalnum():
           goto L0300
        # We found the end of a word. Process it
        found = False
        word = line[start_char:i].lower()
        # Ignore stop words
        if word in stop_words:
           goto L0280
        pair_index = 0
        # Let's see if it already exists
        for pair in word_freqs:
            if word != pair[0]:
               goto L0210
            pair[1] += 1
            found = True
            goto L0220
            L0210:
            pair_index += 1
        L0220:
        if found:
           goto L0230
        word_freqs.append([word, 1])
        goto L0300
        L0230:
        if len(word_freqs) <= 1:
           goto L0300:
        # We may need to reorder
        for n in reversed(range(pair_index)):
            if word_freqs[pair_index][1] <= word_freqs[n][1]:
               goto L0240
            # swap
            word_freqs[n], word_freqs[pair_index] = word_freqs[pair_index], word_freqs[n]
            pair_index = n
            L0240:
        goto L0300
        L0280:
        # Let's reset
        start_char = None
        L0300:
        i += 1

If you do feel a yearning for the good ol days, a goto package is available, enabling developers to write code such as:

from goto import with_goto

@with_goto
def range(start, stop):
    i = start
    result = []

    label .begin
    if i == stop:
        goto .end

    result.append(i)
    i += 1
    goto .begin

    label .end
    return result

Christmas books for 2019

Derek Jones from The Shape of Code

The following are the really, and somewhat, interesting books I read this year. I am including the somewhat interesting books to bulk up the numbers; there are probably more books out there that I would find interesting. I just did not read many books this year, what with Amazon recommends being so user unfriendly, and having my nose to the grindstone finishing a book.

First the really interesting.

I have already written about Good Enough: The Tolerance for Mediocrity in Nature and Society by Daniel Milo.

I have also written about The European Guilds: An economic analysis by Sheilagh Ogilvie. Around half-way through I grew weary, and worried readers of my own book might feel the same. Ogilvie nails false beliefs to the floor and machine-guns them. An admirable trait in someone seeking to dispel the false beliefs in current circulation. Some variety in the nailing and machine-gunning would have improved readability.

Moving on to first half really interesting, second half only somewhat.

“In search of stupidity: Over 20 years of high-tech marketing disasters” by Merrill R. Chapman, second edition. This edition is from 2006, and a third edition is promised, like now. The first half is full of great stories about the successes and failures of computer companies in the 1980s and 1990s, by somebody who was intimately involved with them in a sales and marketing capacity. The author does not appear to be so intimately involved, starting around 2000, and the material flags. Worth buying for the first half.

Now the somewhat interesting.

“Can medicine be cured? The corruption of a profession” by Seamus O’Mahony. All those nonsense theories and practices you see going on in software engineering, it’s also happening in medicine. Medicine had a golden age, when progress was made on finding cures for the major diseases, and now it’s mostly smoke and mirrors as people try to maintain the illusion of progress.

“Who we are and how we got here” by David Reich (a genetics professor who is a big name in the field), is the story of the various migrations and interbreeding of ‘human-like’ and human peoples over the last 50,000 years (with some references going as far back as 300,000 years). The author tries to tell two stories, the story of human migrations and the story of the discoveries made by his and other people’s labs. The mixture of stories did not work for me; the story of human migrations/interbreeding was very interesting, but I was not at all interested in when and who discovered what. The last few chapters went off at a tangent, trying to have a politically correct discussion about identity and race issues. The politically correct class are going to hate this book’s findings.

“The Digital Party: Political organization and online democracy” by Paolo Gerbaudo. The internet has enabled some populist political parties to attract hundreds of thousands of members. Are these parties living up to their promises to be truly democratic and representative of members wishes? No, and Gerbaudo does a good job of explaining why (people can easily join up online, and then find more interesting things to do than read about political issues; only a few hard code members get out from behind the screen and become activists).

Suggestions for books that you think I might find interesting welcome.

Three books discuss three small data sets

Derek Jones from The Shape of Code

During the early years of a new field, experimental data relating to important topics can be very thin on the ground. Ever since the first computer was built, there has been a lot of data on the characteristics of the hardware. Data on the characteristics of software, and the people who write it has been (and often continues to be) very thin on the ground.

Books are sometimes written by the researchers who produce the first data associated with an important topic, even if the data set is tiny; being first often generates enough interest for a book length treatment to be considered worthwhile.

As a field progresses lots more data becomes available, and the discussion in subsequent books can be based on findings from more experiments and lots more data

Software engineering is a field where a few ‘first’ data books have been published, followed by silence, or rather lots of arm waving and little new data. The fall of Rome has been followed by a 40-year dark-age, from which we are slowly emerging.

Three of these ‘first’ data books are:

  • “Man-Computer Problem Solving” by Harold Sackman, published in 1970, relating to experimental data from 1966. The experiments investigated the impact of two different approaches to developing software, on programmer performance (i.e., batch processing vs. on-line development; code+data). The first paper on this work appeared in an obscure journal in 1967, and was followed in the same issue by a critique pointing out the wide margin of uncertainty in the measurements (the critique agreed that running such experiments was a laudable goal).

    Failing to deal with experimental uncertainty is nothing compared to what happened next. A 1968 paper in a widely read journal, the Communications of the ACM, contained the following table (extracted from a higher quality scan of a 1966 report by the same authors, and available online).

    Developer performance ratios.

    The tale of 1:28 ratio of programmer performance, found in an experiment by Grant/Sackman, took off (the technical detail that a lot of the difference was down to the techniques subjects’ used, and not the people themselves, got lost). The Grant/Sackman ‘finding’ used to be frequently quoted in some circles (or at least it did when I moved in them, I don’t know often it is cited today). In 1999, Lutz Prechelt wrote an expose on the sorry tale.

    Sackman’s book is very readable, and contains lots of details and data not present in the papers, including survey data and a discussion of the intrinsic uncertainties associated with the experiment; it also contains the table above.

  • “Software Engineering Economics” by Barry W. Boehm, published in 1981. I wrote about the poor analysis of the data contained in this book a few years ago.

    The rest of this book contains plenty of interesting material, and even sounds modern (because books moving the topic forward have not been written).

  • “Program Evolution: Process of Software Change” edited by M. M. Lehman and L. A. Belady, published in 1985, relating to experimental data from 1977 and before. Lehman and Belady managed to obtain data relating to 19 releases of an IBM software product (yes, 19, not nineteen-thousand); the data was primarily the date and number of modules contained in each release, plus less specific information about number of statements. This data was sliced and diced every which way, and the book contains many papers with the same data appearing in the same plot with different captions (had the book not been a collection of papers it would have been considerably shorter).

    With a lot less data than Isaac Newton had available to formulate his three laws, Lehman and Belady came up with five, six, seven… “laws of software evolution” (which themselves evolved with the publication of successive papers).

    The availability of Open source repositories means there is now a lot more software system evolution data available. Lehman’s laws have not stood the test of more data, although people still cite them every now and again.

Natural elimination, or the survival of the good enough

Derek Jones from The Shape of Code

Thanks to Darwin, the world is full of people who think that evolution, in nature, works by: natural selection, or the survival of the fittest. I thought this until I read “Good Enough: The Tolerance for Mediocrity in Nature and Society” by Daniel Milo.

Milo makes a very convincing case that nature actually works by: natural elimination, or the survival of the good enough.

Why might Darwin have gone with natural selection in his book, On the Origin of Species? Milo makes the point that the only real evidence that Darwin had to work with was artificial selection, that is the breeding of farm animals and domestic pets to select for traits that humans found desirable. Darwin’s visit to the Galápagos islands triggered a way of thinking, it did not provide him with the evidence he needed; Darwin’s Finches have become a commonly cited example of natural selection at work, but while Darwin made the observations it was not until 80 years later that somebody else spotted their relevance.

The Origin of Species, or to use its full title: “On the Origin of Species by means of natural selection, or the preservation of favored races in the struggle for life.” is full of examples and terminology relating to artificial selection.

Natural selection, or natural elimination, isn’t the result the same?

Natural selection implies an optimization process, e.g., breeders selecting for a strain of cows that produce the most milk.

Natural elimination is a good enough process, i.e., a creature needs a collection of traits that are good enough for them to create the next generation.

A long-standing problem with natural selection is that it fails to explain the diversity present in a natural population of some breed of animal (there is very little diversity in each breed of farm animal, they have been optimized for consistency). Diversity is not a problem for natural elimination, which does not reduce differences in its search for fitness.

The diversity produced as a consequence of natural elimination creates a population containing many neutral traits (i.e., characteristics that have no positive or negative impact on continuing survival). When a significant change in the environment occurs, one or more of the neutral traits may suddenly have positive or negative survival consequences; the creatures with the positive traits have opportunity time to adapt to the changed environment. A population whose members possess a diverse range of neutral traits has a higher chance of long-term survival than a population where diversity has been squeezed in the quest for the fittest.

I think that natural elimination also applies within software ecosystems. Commercial products survive if enough customers buy them, software developers need good enough know-how to get the job done.

I’m sure customers would prefer software ecosystems to operate on the principle of survival of the fittest (it reduces their costs). Over the long term is society best served by diverse software ecosystems or softwaremonocultures? Diversity is a way of encouraging competition, but over time there is diminishing returns on the improvements.

Christmas books for 2018

Derek Jones from The Shape of Code

The following are the really interesting books I read this year (only one of which was actually published in 2018, everything has to work its way through several piles). The list is short because I did not read many books and/or there is lots of nonsense out there.

The English and their history by Robert Tombs. A hefty paperback, at nearly 1,000 pages, it has been the book I read on train journeys, for most of this year. Full of insights, along with dull sections, a narrative that explains lots of goings-on in a straight-forward manner. I still have a few hundred pages left to go.

The mind is flat by Nick Chater. We experience the world through a few low bandwidth serial links and the brain stitches things together to make it appear that our cognitive hardware/software is a lot more sophisticated. Chater’s background is in cognitive psychology (these days he’s an academic more connected with the business world) and describes the experimental evidence to back up his “mind is flat” model. I found that some of the analogues dragged on too long.

In the readable social learning and evolution category there is: Darwin’s unfinished symphony by Leland and The secret of our success by Henrich. Flipping through them now, I cannot decide which is best. Read the reviews and pick one.

Group problem solving by Laughin. Eye opening. A slim volume, packed with data and analysis.

I have already written about Experimental Psychology by Woodworth.

The Digital Flood: The Diffusion of Information Technology Across the U.S., Europe, and Asia by Cortada. Something of a specialist topic, but if you are into the diffusion of technology, this is surely the definitive book on the diffusion of software systems (covers mostly hardware).

Practical ecosystem books for software engineers

Derek Jones from The Shape of Code

So you have read my (draft) book on evidence-based software engineering and want to learn more about ecosystems. What books do I suggest?

Biologists have been studying ecosystems for a long time, and more recently social scientists have been investigating cultural ecosystems. Many of the books written in these fields are oriented towards solving differential equations and are rather subject specific.

The study of software ecosystems has been something of a niche topic for a long time. Problems for researchers have included gaining access to ecosystems and the seeming proliferation of distinct ecosystems. The state of ecosystem research in software engineering is rudimentary; historians are starting to piece together what has happened.

Most software ecosystems are not even close to being in what might be considered a steady state. Eventually most software will be really old, and this will be considered normal (“Shock Of The Old: Technology and Global History since 1900″ by Edgerton; newness is a marketing ploy to get people to buy stuff). In the meantime, I have concentrated on the study of ecosystems in a state of change.

Understanding ecosystems is about understanding how the interaction of participant’s motivation, evolves the environment in which they operate.

“Modern Principles of Economics” by Cowen and Tabarrok, is a very readable introduction to economics. Economics might be thought of as a study of the consequences of optimizing the motivation of maximizing return on investment. “Principles of Corporate Finance” by Brealey and Myers, focuses on the topic in its title.

“The Control Revolution: Technological and Economic Origins of the Information Society” by Beniger: the ecosystems in which software ecosystems coexist and their motivations.

“Evolutionary dynamics: exploring the equations of life” by Nowak, is a readable mathematical introduction to the subject given in the title.

“Mathematical Models of Social Evolution: A Guide for the Perplexed” by McElreath and Boyd, is another readable mathematical introduction, but focusing on social evolution.

“Social Learning: An Introduction to Mechanisms, Methods, and Models” by Hoppitt and Laland: developers learn from each other and from their own experience. What are the trade-offs for the viability of an ecosystem that preferentially contains people with specific ways of learning?

“Robustness and evolvability in living systems” by Wagner, survival analysis of systems built from components (DNA in this case). Rather specialised.

Books with a connection to technology ecosystems.

“Increasing returns and path dependence in the economy” by Arthur, is now a classic, containing all the basic ideas.

“The red queen among organizations” by Barnett, includes a chapter on computer manufacturers (has promised me data, but busy right now).

“Information Foraging Theory: Adaptive Interaction with Information” by Pirolli, is an application of ecosystem know-how, i.e., how best to find information within a given environment. Rather specialised.

“How Buildings Learn: What Happens After They’re Built” by Brand, yes building are changed just like software and the changes are just as messy and expensive.

Several good books have probably been omitted, because I failed to spot them sitting on the shelf. Suggestions for books covering topics I have missed welcome, or your own preferences.

Practical psychology books for software engineers

Derek Jones from The Shape of Code

So you have read my (draft) book on evidence-based software engineering and want to learn more about human psychology. What books do I suggest?

I wrote a book about C that attempted to use results from cognitive psychology to understand developer characteristics. This work dates from around 2000, and some of my book choices may have been different, had I studied the subject 10 years later. Another consequence is that this list is very weak on social psychology.

I own all the following books, but it may have been a few years since I last took them off the shelf.

There are two very good books providing a broad introduction: “Cognitive psychology and its implications” by Anderson, and “Cognitive psychology: A student’s handbook” by Eysenck and Keane. They have both been through many editions, and buying a copy that is a few editions earlier than current, saves money for little loss of content.

“Engineering psychology and human performance” by Wickens and Hollands, is a general introduction oriented towards stuff that engineering requires people to do.

Brain functioning: “Reading in the brain” by Dehaene (a bit harder going than “The number sense”). For those who want to get down among the neurons “Biological psychology” by Kalat.

Consciouness: This issue always comes up, so let’s kill it here and now: “The illusion of conscious will” by Wegner, and “The mind is flat” by Chater.

Decision making: What is the difference between decision making and reasoning? In psychology those with a practical orientation study decision making, while those into mathematical logic study reasoning. “Rational choice in an uncertain world” by Hastie and Dawes, is a general introduction; “The adaptive decision maker” by Payne, Bettman and Johnson, is a readable discussion of decision making models. “Judgment under Uncertainty: Heuristics and Biases” by Kahneman, Slovic and Tversky, is a famous collection of papers that kick started the field at the start of the 1980s.

Evolutionary psychology: “Human evolutionary psychology” by Barrett, Dunbar and Lycett. How did we get to be the way we are? Watch out for the hand waving (bones can be dug up for study, but not the software of our mind), but it weaves a coherent’ish story. If you want to go deeper, “The Adapted Mind: Evolutionary Psychology and the Generation of Culture” by Barkow, Tooby and Cosmides, is a collection of papers that took the world by storm at the start of the 1990s.

Language: “The psychology of language” by Harley, is the book to read on psycholinguistics; it is engrossing (although I have not read the latest edition).

Memory: I have almost a dozen books discussing memory. What these say is that there are a collection of memory systems having various characteristics; which is what the chapters in the general coverage books say.

Modeling: So you want to model the human brain. ACT-R is the market leader in general cognitive modeling. “Bayesian cognitive modeling” by Lee and Wagenmakers, is a good introduction for those who prefer a more abstract approach (“Computational modeling of cognition” by Farrell and Lewandowsky, is a big disappointment {they have written some great papers} and best avoided).

Reasoning: The study of reasoning is something of a backwater in psychology. Early experiments showed that people did not reason according to the rules of mathematical logic, and this was treated as a serious fault (whose fault it was, shifted around). Eventually most researchers realised that the purpose of reasoning was to aid survival and reproduction, not following the recently (100 years or so) invented rules of mathematical logic (a few die-hards continue to cling to the belief that human reasoning has a strong connection to mathematical logic, e.g., Evans and Johnson-Laird; I have nearly all their books, but have not inflicted them on the local charity shop yet). Gigerenzer has written several good books: “Adaptive thinking: Rationality in the real world” is a readable introduction, also “Simple heuristics that make us smart”.

Social psychology: “Social learning” by Hoppitt and Laland, analyzes the advantages and disadvantages of social learning; “The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter” by Henrich, is a more populist book (by a leader in the field).

Vision: “Visual intelligence” by Hoffman is a readable introduction to how we go about interpreting the photons entering our eyes, while “Graph design for the eye and mind” by Kosslyn is a rule based guide to visual presentation. “Vision science: Photons to phenomenology” by Palmer, for those who are really keen.

Several good books have probably been omitted, because I failed to spot them sitting on the shelf. Suggestions for books covering topics I have missed welcome, or your own preferences.

Practical statistics books for software engineers

Derek Jones from The Shape of Code

So you have read my (draft) book on evidence-based software engineering and want to learn more about the statistical techniques used, but are not interested lots of detailed mathematics. What books do I suggest?

All the following books are sitting on the shelf next to where I write (not that they get read that much these days).

Before I took the training wheels off my R usage, my general go to book was (I still look at it from time to time): “The R Book” by Crawley, second edition; “R in Action” by Kabacoff is a good general read.

In alphabetical subject order:

Categorical data: “Categorical Data Analysis” by Agresti, the third edition is a weighty tomb (in content and heaviness). Plenty of maths+example; more of a reference.

Compositional data: “Analyzing compositional data with R” by van den Boogaart and Tolosana-Delgado, is more or less the only book of its kind. Thankfully, it is quite good.

Count data: “Modeling count data” by Hilbe, may be more than you want to know about count data. Readable.

Circular data: “Circular statistics in R” by Pewsey, Neuhauser and Ruxton, is the only non-pure theory book available. The material seems to be there, but is brief.

Experiments: “Design and analysis of experiments” by Montgomery.

General: “Applied linear statistical models” by Kutner, Nachtsheim, Neter and Li, covers a wide range of topics (including experiments) using a basic level of mathematics.

Mixed-effects models: “Mixed-effects models in S and S-plus” by Pinheiro and Bates, is probably the book I prefer; “Mixed effects models and extensions in ecology with R” by Zuur, Ieno, Walker, Saveliev and Smith, is another view on an involved topic (plus lots of ecological examples).

Modeling: “Statistical rethinking” by McElreath, is full of interesting modeling ideas, using R and Stan. I wish I had some data to try out some of these ideas.

Regression analysis: “Applied Regression Analysis and Generalized Linear Models” by Fox, now in its third edition (I also have the second edition). I found this the most useful book, of those available, for a more detailed discussion of regression analysis. Some people like “Regression modeling strategies” by Harrell, but this does not appeal to me.

Survival analysis: “Introducing survival and event history analysis” by Mills, is a readable introduction covering everything; “Survival analysis” by Kleinbaum and Klein, is full of insights but more of a book to dip into.

Time series: The two ok books are: “Time series analysis and its application: with R examples” by Shumway and Stoffler, contains more theory, while “Time series analysis: with applications in R” by Cryer and Chan, contains more R code.

There are lots of other R/statistics books on my shelves (just found out I have 31 of Springer’s R books), some ok, some not so. I have a few ‘programming in R’ style books; if you are a software developer, R the language is trivial to learn (its library is another matter).

Suggestions for books covering topics I have missed welcome, or your own preferences (as a software developer).

Historians of computing

Derek Jones from The Shape of Code

Who are the historians of the computing? The requirement I used for deciding who qualifies (for this post), is that the person has written multiple papers on the subject over a period that is much longer than their PhD thesis (several people have written history of some aspect of computing PhDs and then gone on to research other areas).

Maarten Bullynck. An academic who is a historian of mathematics and has become interested in software; use HAL to find his papers, e.g., What is an Operating System? A historical investigation (1954–1964).

Martin Campbell-Kelly. An academic who has spent his research career investigating computing history, primarily with a software orientation. Has written extensively on a wide variety of software topics. His book “From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry” is still on my pile of books waiting to be read (but other historian cite it extensively). His thesis: “Foundations of computer programming in Britain, 1945-55″, can be freely downloadable from the British Library; registration required.

James W. Cortada. Ex-IBM (1974-2012) and now working at the Charles Babbage Institute. Written extensively on the history of computing. More of a hardware than software orientation. Written lots of detail oriented books and must have pole position for most extensive collection of material to cite (his end notes are very extensive). His “Buy The Digital Flood: The Diffusion of Information Technology Across the U.S., Europe, and Asia” is likely to be the definitive work on the subject for some time to come. For me this book is spoiled by the author towing the company line in his analysis of the IBM antitrust trial; my analysis of the work Cortada cites reaches the opposite conclusion.

Nathan Ensmenger. An academic; more of a people person than hardware/software. His paper Letting the Computer Boys Take Over contains many interesting insights. His book The Computer Boys Take Over Computers, Programmers, and the Politics of Technical Expertise is a combination of topics that have been figured and back with references and topics still being figured out (I wish he would not cite Datamation, a trade mag back in the day, so often).

Michael S. Mahoney. An academic who is sadly no longer with us. A historian of mathematics before becoming involved with primarily software.

Jeffrey R. Yost. An academic. I have only read his book “Making IT Work: A history of the computer services industry”, which was really a collection of vignettes about people, companies and events; needs some analysis. Must try to track down some of his papers (which are not available via his web page :-(.

Who have I missed? This list is derived from papers/books I have encountered while working on a book, not an active search for historians. Suggestions welcome.

Computer books your great grandfather might have read

Derek Jones from The Shape of Code

I have been reading two very different computer books written for a general readership: Giant Brains or Machines that Think, published in 1949 (with a retrospective chapter added in 1961) and LET ERMA DO IT, published in 1956.

‘Giant Brains’ by Edmund Berkeley, was very popular in its day.

Berkeley marvels at a computer performing 5,000 additions per second; performing all the calculations in a week that previously required 500 human computers (i.e., people using mechanical adding machines) working 40 hours per week. His mind staggers at the “calculating circuits being developed” that can perform 100,00 additions a second; “A mechanical brain that can do 10,000 additions a second can very easily finish almost all its work at once.”

The chapter discussing the future, “Machines that think, and what they might do for men”, sees Berkeley struggling for non-mathematical applications; a common problem with all new inventions. Automatic translator and automatic stenographer (typist who transcribe dictation) are listed. There is also a chapter on social control, which is just as applicable today.

This was the first widely read book to promote Shannon‘s idea of using the algebra invented by George Boole to analyze switching circuits symbolically (THE 1940 Masters thesis).

The ‘ERMA’ book paints a very rosy picture of the future with computer automation removing the drudgery that so many jobs require; it is so upbeat. A year later the USSR launched Sputnik and things suddenly looked a lot less rosy.