Programming Error Quotes

He knew what he was experiencing was a basic error in programming, and he wished he could open up his brain and delete the bad code. Unfortunately, the human brain is every bit as closed a system as a Mac.

I didn’t even know what was wrong. Everything. Myself. I didn’t know. How come everyone else could function and I couldn’t? How could everyone live properly yet I had some sort of error in my programming?

Software testing is a sport like hunting, it's bughunting.

Never test for an error condition you don't know how to handle.

From a shamanic perspective, the psychic blockade that prevents otherwise intelligent adults from considering the future of our world - our obvious lack of future, if we continue on our present path - reveals an occult dimension. It is like a programming error written into the software designed for the modern mind, which has endless energy to spend on the trivial and treacly, sports statistic or shoe sale, but no time to spare for the torments of the Third World, for the mass extinction of species to perpetuate a way of life without a future, for the imminent exhaustion of fossil fuel reserves, or for the fine print of the Patriot Act. This psychic blockade is reinforced by a vast propaganda machine spewing out crude as well as sophisticated distractions, encouraging individuals to see themselves as alienated spectators of their culture, rather than active participants in a planetary ecology.

Computers need to bond with their users to avoid panicking with fatal errors, like skittish horses at spring thunder.

Trying to improve software quality by increasing the amount of testing is like trying to lose weight by weighing yourself more often. What you eat before you step onto the scale determines how much you will weigh, and the software-development techniques you use determine how many errors testing will find.

I think that it’s extraordinarily important that we in computer science keep fun in computing. When it started out it was an awful lot of fun. Of course the paying customers got shafted every now and then and after a while we began to take their complaints seriously. We began to feel as if we really were responsible for the successful error-free perfect use of these machines. I don’t think we are. I think we’re responsible for stretching them setting them off in new directions and keeping fun in the house. I hope the field of computer science never loses its sense of fun. Above all I hope we don’t become missionaries. Don’t feel as if you’re Bible sales-men. The world has too many of those already. What you know about computing other people will learn. Don’t feel as if the key to successful computing is only in your hands. What’s in your hands I think and hope is intelligence: the ability to see the machine as more than when you were first led up to it that you can make it more.

In 1953, Allen Dulles, then director of the USA Central Intelligence Agency (CIA), named Dr Sidney Gottlieb to direct the CIA's MKULTRA programme, which included experiments conducted by psychiatrists to create amnesia, new dissociated identities, new memories, and responses to hypnotic access codes. In 1972, then-CIA director Richard Helms and Gottlieb ordered the destruction of all MKULTRA records. A clerical error spared seven boxes, containing 1738 documents, over 17,000 pages. This archive was declassified through a Freedom of Information Act Request in 1977, though the names of most people, universities, and hospitals are redacted. The CIA assigned each document a number preceded by "MORI", for "Managament of Officially Released Information", the CIA's automated electronic system at the time of document release. These documents, to be referenced throughout this chapter, are accessible on the Internet (see: abuse-of-power (dot) org/modules/content/index.php?id=31). The United States Senate held a hearing exposing the abuses of MKULTRA, entitled "Project MKULTRA, the CIA's program of research into behavioral modification" (1977).

I like my code to be elegant and efficient. The logic should be straightforward to make it hard for bugs to hide, the dependencies minimal to ease maintenance, error handling complete according to an articulated strategy, and performance close to optimal so as not to tempt people to make the code messy with unprincipled optimizations. Clean code does one thing well. -Bjarne Stroustrup, inventor of C++ and author of The C++ Programming Language

[A]s a species, we are very poor at programming. Our brains are built to understand other humans, not computers. We’re terrible at forcing our minds into the precise modes of thought needed to interact with a computer, and we consistently make errors when we try. That's why computer science and programming degrees take such time and dedication to acquire: we are literally learning how to speak to an alien mind, of a kind that has not existed on Earth until very recently.

The realization came over me with full force that a good part of the remainder of my life was going to be spent in finding errors in my own programs.

There are two ways to write error-free programs; only the third one works.” —Alan J. Perlis

The universities are an absolute wreck right now, because for decades, any graduate student in the humanities who had independent thinking was driven out. There was no way to survive without memorizing all these stupid bromides with this referential bowing to these over-inflated figures like Lacan, Derrida, Foucault, and so on. Basically, it's been a tyranny in the humanities, because the professors who are now my age – who are the baby boomer professors, who made their careers on the back of Foucault and so on – are determined that that survive. So you have a kind of vampirism going on. So I've been getting letters for 25 years since Sexual Personae was released in 1990, from refugees from the graduate schools. It's been a terrible loss. One of my favorite letters was early on: a woman wrote to me, she was painting houses in St. Louis, she said that she had wanted a career as a literature professor and had gone into the graduate program in comparative literature at Berkeley. And finally, she was forced to drop out because, she said, every time she would express enthusiasm for a work they were studying in the seminar, everyone would look at her as if she had in some way created a terrible error of taste. I thought, 'Oh my God', see that's what's been going on – a pretentious style of superiority to the text. [When asked what can change this]: Rebellion! Rebellion by the grad students. This is what I'm trying to foment. We absolutely need someone to stand up and start criticizing authority figures. But no; this generation of young people have been trained throughout middle school and high school and college to be subservient to authority.

Why is it that we reward programmers who work all night to remove the errors they put into their programs, or managers who make drastic organizational changes to resolve the crises their poor management has created? Why not reward the programmers who design so well that they don’t have dramatic errors, and managers whose organizations stay out of crisis mode? Organizing

At the first such gathering, I politely sat with them for half an hour, drank some vodka, and even recited a toast about how great it was that Gulya had such great friends. This proved to be a tactical error, since afterward Gulya wanted me to drink vodka and recite toasts with them every night, which was not compatible with my program of study of the great Uzbek language.

Having a fear of failure means you believe that even the smallest error could be evidence that you are a worthless and awful person. Having a fear of being imperfect means that it is difficult for you to accept yourself as you are—imperfect and, therefore, perfectly human—and so you interpret any criticism, rejection, or judgment by others as a threat to your very tenuous grasp on perfection. Having a fear of impossible expectations means fearing that even after you’ve worked hard and achieved the goals set for you, your only reward will be continually higher and more difficult goals to achieve, with no rest and no time to savor your achievements.

The most tragic error into which older people can fall is one that is common among educators and politicians. It is to use youth as scapegoats for the sins of their elders. Is the nation wasting its young men and its honor in an unjust war? Never mind — direct your frustration at the long-haired young people who are shouting in the streets that the war must end. Curse them as hippies and immoral, dirty fanatics; after all, we older Americans could not have been wrong about anything important, because our hearts are all in the right place and God is always on our side, so anyone who opposes us must be insane, and probably in the pay of the godless Communists. Youth is in the process of being classed with the dark- skinned minorities as the object of popular scorn and hatred. It    is   as  if  Americans  have  to  have  a  "nigger,"  a  target                             for its hidden frustrations and guilt. Without someone to blame, like the Communists abroad and the young and black at home, middle America would be forced to consider whether all the problems of our time were in any way its own fault. That is the one thing it could never stand to do. Hence, it finds scapegoats. Few adults, I am afraid, will ever break free of the crippling attitudes that have been programmed into their personalities – racism, self-righteousness, lack of concern for the losers of the world, and an excessive regard for property. One reason, as I have noted, is that they do not know they are like this, and that they proclaim ideals that are the reverse of many of their actions. Such hypocrisy, even if it is unconscious, is the real barrier between them and their children.

There is humility in confession. A recognition of flaws. To hear myself say out loud these shameful secrets meant I acknowledged my flaws. I also for the first time was given the opportunity to contextualize anew the catalogue of beliefs and prejudices, simply by exposing them to another, for the first time hearing the words ‘Yes, but have you looked at it this way?’ This was a helpful step in gaining a new perspective on my past, and my past was a significant proportion of who I believed myself to be. It felt like I had hacked into my own past. Unravelled all the erroneous and poisonous information I had unconsciously lived with and lived by and with necessary witness, the accompaniment of another man, reset the beliefs I had formed as a child and left unamended through unnecessary fear. Suddenly my fraught and freighted childhood became reasonable and soothed. ‘My mum was doing her best, so was my dad.’ Yes, people made mistakes but that’s what humans do, and I am under no obligation to hoard these errors and allow them to clutter my perception of the present. Yes, it is wrong that I was abused as a child but there is no reason for me to relive it, consciously or unconsciously, in the way I conduct my adult relationships. My perceptions of reality, even my own memories, are not objective or absolute, they are a biased account and they can be altered. It is possible to reprogram your mind. Not alone, because a tendency, a habit, an addiction will always reassert by its own invisible momentum, like a tide. With this program, with the support of others, and with this mysterious power, this new ability to change, we achieve a new perspective, and a new life.

Intelligence involves a great deal more than the ability to follow rules ( which is what chess-playing program does). It is also the ability to make up the rules for oneself, when they are needed, or to learn new rules through trial and error.

I think that it's extraordinarily important that we in computer science keep fun in computing. When it started out, it was an awful lot of fun. Of course, the paying customers got shafted every now and then, and after a while we began to take their complaints seriously. We began to feel as if we really were responsible for the successful, error-free perfect use of these machines. I don't think we are. I think we're responsible for stretching them, setting them off in new directions, and keeping fun in the house. I hope the field of computer science never loses its sense of fun. Above all, I hope we don't become missionaries. Don't feel as if you're Bible salesmen. The world has too many of those already. What you know about computing other people will learn. Don't feel as if the key to successful computing is only in your hands. What's in your hands, I think and hope, is intelligence: the ability to see the machine as more than when you were first led up to it, that you can make it more.

Sam knew the foot was gone. He could see it was gone. He knew what he was experiencing was a basic error in programming, and he wished he could open up his brain and delete the bad code. Unfortunately, the human brain is every bit as closed a system as a Mac.

The goal is to minimize the amount of a program you have to think about at any one time. You might think of this as mental juggling—the more mental balls the program requires you to keep in the air at once, the more likely you'll drop one of the balls, leading to a design or coding error.

efficiently means providing slots in our schedules where we can maintain an attentional set for an extended period. This allows us to get more done and finish up with more energy. Related to the manager/worker distinction is that the prefrontal cortex contains circuits responsible for telling us whether we’re controlling something or someone else is. When we set up a system, this part of the brain marks it as self-generated. When we step into someone else’s system, the brain marks it that way. This may help explain why it’s easier to stick with an exercise program or diet that someone else sets up: We typically trust them as “experts” more than we trust ourselves. “My trainer told me to do three sets of ten reps at forty pounds—he’s a trainer, he must know what he’s talking about. I can’t design my own workout—what do I know?” It takes Herculean amounts of discipline to overcome the brain’s bias against self-generated motivational systems. Why? Because as with the fundamental attribution error we saw in Chapter 4, we don’t have access to others’ minds, only our own. We are painfully aware of all the fretting and indecision, all the nuances of our internal decision-making process that led us to reach a particular conclusion. (I really need to get serious about exercise.) We don’t have access to that (largely internal) process in others, so we tend to take their certainty as more compelling, in many cases, than our own. (Here’s your program. Do it every day.)

One of the paradoxes of defensive programming is that during development, you'd like an error to be noticeable—you'd rather have it be obnoxious than risk overlooking it. But during production, you'd rather have the error be as unobtrusive as possible, to have the program recover or fail gracefully.

one of the essential insights gained by Turing as he observed his “children” was that if machines were ever to advance toward true intelligence, they would have to be fallible: they would need to be capable not only of error and of deviation from their original programming but also of random and even nonsensical behavior. Turing believed that such randomness would play an important part in intelligent machinery, because it allows for novel and unpredictable responses, creating a large variety of possibilities among which a search program could then find the appropriate action for each particular circumstance.

One of the greatest problems for systems programmers (the people who write compilers, interpreters, assemblers, and other programs to be used by many people) is to figure out how to write error-detecting routines in such a way that the messages which they feed to the user whose program has a "bug" provide high-level, rather than low-level, descriptions of the problem. it is an interesting reversal that when something goes wrong in a genetic "program" (e.g., a mutation), the "bug" is manifest only to people on a high level - namely on the phenotype level, not the genotype level. Actually, modern biology uses mutations as one of its principal windows onto genetic processes, because of their multilevel traceability.

Inheritance has recently fallen out of favor as a programming design solution in many programming languages because it’s often at risk of sharing more code than necessary. Subclasses shouldn’t always share all characteristics of their parent class but will do so with inheritance. This can make a program’s design less flexible. It also introduces the possibility of calling methods on subclasses that don’t make sense or that cause errors because the methods don’t apply to the subclass. In addition, some languages will only allow a subclass to inherit from one class, further restricting the flexibility of a program’s design. For these reasons, Rust takes a different approach, using trait objects instead of inheritance.

I find no reason to think that aging is genetically determined. Genes do not provide information for the development of the individual beyond growth and the reproductive process in which the genes are transmitted to the next generation. Once past the reproductive stage, the individual has served the purposes of preservation of the species, and then he is on his own. The wrinkled human face is the victim of gravity and of cumulative errors in the reproduction of cells. Since aging is not programed, but is a badly improvised interference with youthful beauty, we have improvised an operation to counteract its effects. Aging is a form of misinformation. If we get the facts right, you will be able to read it in our faces. ("Motherhood")

In testing primality of very large numbers chosen at random, the chance of stumbling upon a value that fools the Fermat test is less than the chance that cosmic radiation will cause the computer to make an error in carrying out a "correct" algorithm. Considering an algorithm to be inadequate for the first reason but not for the second illustrates the difference between mathematics and engineering.

Without sex, the only way for bacteria to adapt is through mutation, which is caused by reproductive error or environmental damage. Most mutations are hurtful; they make for even less successful bacteria—though eventually, with luck, a mutation would arise that made for a more heat-resistant bacterium. Asexual adaptation is problematic because the dictate of the world, “Change or die,” runs directly counter to one of the primary dictates of life: “Maintain the integrity of the genome.” In engineering, this type of clash is called a coupled design. Two functions of a system clash so that it is not possible to adjust one without negatively affecting the other. In sexual reproduction, by contrast, the inherent scrambling, or recombination, affords a vast scope for change, yet still maintains genetic integrity.

We are about to study the idea of a computational process. Computational processes are abstract beings that inhabit computers. As they evolve, processes manipulate other abstract things called data. The evolution of a process is directed by a pattern of rules called a program. People create programs to direct processes. In effect, we conjure the spirits of the computer with our spells. A computational process is indeed much like a sorcerer's idea of a spirit. It cannot be seen or touched. It is not composed of matter at all. However, it is very real. It can perform intellectual work. It can answer questions. It can affect the world by disbursing money at a bank or by controlling a robot arm in a factory. The programs we use to conjure processes are like a sorcerer's spells. They are carefully composed from symbolic expressions in arcane and esoteric programming languages that prescribe the tasks we want our processes to perform. A computational process, in a correctly working computer, executes programs precisely and accurately. Thus, like the sorcerer's apprentice, novice programmers must learn to understand and to anticipate the consequences of their conjuring. Even small errors (usually called bugs or glitches) in programs can have complex and unanticipated consequences.

Code should be obvious: When someone needs to make a change, they should be able to find the code to be changed easily and to make the change quickly without introducing any errors. A healthy code base maximizes our productivity, allowing us to build more features for our users both faster and more cheaply. To keep code healthy, pay attention to what is getting between the programming team and that ideal, then refactor to get closer to the ideal. But the most important thing to learn from this example is the rhythm of refactoring. Whenever I’ve shown people how I refactor, they are surprised by how small my steps are, each step leaving the code in a working state that compiles and passes its tests. I was just as surprised myself when Kent Beck showed me how to do this in a hotel room in Detroit two decades ago. The key to effective refactoring is recognizing that you go faster when you take tiny steps, the code is never broken, and you can compose those small steps into substantial changes. Remember that—and the rest is silence.

There is a simple reason why you should commit yourself to writing programs that are free of errors from the very start. It is that you will never be able to establish that a program has no errors in it by testing. Since there is no way to be certain that you have found the last error, your real opportunity to gain confidence in a program is to never find the first error. The ultimate faith you can have in one of your programs is in the thought process that created it. With every error you find in testing and use, that faith is undermined.

In 2003, a Dutch clinical psychologist named Christof van Nimwegen began a fascinating study of computer-aided learning that a BBC writer would later call “one of the most interesting examinations of current computer use and the potential downsides of our increasing reliance on screen-based interaction with information systems.”26 Van Nimwegen had two groups of volunteers work through a tricky logic puzzle on a computer. The puzzle involved transferring colored balls between two boxes in accordance with a set of rules governing which balls could be moved at which time. One of the groups used software that had been designed to be as helpful as possible. It offered on-screen assistance during the course of solving the puzzle, providing visual cues, for instance, to highlight permitted moves. The other group used a bare-bones program, which provided no hints or other guidance. In the early stages of solving the puzzle, the group using the helpful software made correct moves more quickly than the other group, as would be expected. But as the test proceeded, the proficiency of the members of the group using the bare-bones software increased more rapidly. In the end, those using the unhelpful program were able to solve the puzzle more quickly and with fewer wrong moves. They also reached fewer impasses—states in which no further moves were possible—than did the people using the helpful software. The findings indicated, as van Nimwegen reported, that those using the unhelpful software were better able to plan ahead and plot strategy, while those using the helpful software tended to rely on simple trial and error. Often, in fact, those with the helpful software were found “to aimlessly click around” as they tried to crack the puzzle.

If the application is event-driven, it can be decoupled into multiple self-contained components. This helps us become more scalable, because we can always add new components or remove old ones without stopping or breaking the system. If errors and failures are passed to the right component, which can handle them as notifications, the application can become more fault-tolerant or resilient. So if we build our system to be event-driven, we can more easily achieve scalability and failure tolerance, and a scalable, decoupled, and error-proof application is fast and responsive to users.

Why do we focus on certain things at the expense of others? We will risk our lives to save a person from drowning, yet not make a donation that could save dozens of children from starvation. We install solar panels when their impact on CO2 emissions is minimal - and indeed may have a net negative effect if manufacturing and installation are taken into account - rather than contributing to more efficient infrastructure projects. I consider my own decision-making in these areas to be more rational than that of most people but I also make errors of the same kind. We are genetically programmed to react to stimuli in our immediate vicinity. Responding to complex issues that we can not perceive directly requires the application of reasoning, which is less powerful than instinct.

Everything we do and say will either underline or undermine our discipleship process. As long as there is one unsaved person on my campus or in my city, then my church is not big enough. One of the underlying principles of our discipleship strategy is that every believer can and should make disciples. When a discipleship process fails, many times the fatal flaw is that the definition of discipleship is either unclear, unbiblical, or not commonly shared by the leadership team. Write down what you love to do most, and then go do it with unbelievers. Whatever you love to do, turn it into an outreach. You have to formulate a system that is appropriate for your cultural setting. Writing your own program for making disciples takes time, prayer, and some trial and error—just as it did with us. Learn and incorporate ideas from other churches around the world, but only after modification to make sure the strategies make sense in our culture and community. Culture is changing so quickly that staying relevant requires our constant attention. If we allow ourselves to be distracted by focusing on the mechanics of our own efforts rather than our culture, we will become irrelevant almost overnight. The easiest and most common way to fail at discipleship is to import a model or copy a method that worked somewhere else without first understanding the values that create a healthy discipleship culture. Principles and process are much more important than material, models, and methods. The church is an organization that exists for its nonmembers. Christianity does not promise a storm-free life. However, if we build our lives on biblical foundations, the storms of life will not destroy us. We cannot have lives that are storm-free, but we can become storm-proof. Just as we have to figure out the most effective way to engage our community for Christ, we also have to figure out the most effective way to establish spiritual foundations in each unique context. There is really only one biblical foundation we can build our lives on, and that is the Lord Jesus Christ. Pastors, teachers, and church staff believe their primary role is to serve as mentors. Their task is to equip every believer for the work of the ministry. It is not to do all the ministry, but to equip all the people to do it. Their top priority is to equip disciples to do ministry and to make disciples. Do you spend more time ministering to people or preparing people to minister? No matter what your church responsibilities are, you can prepare others for the same ministry. Insecurity in leadership is a deadly thing that will destroy any organization. It drives pastors and presidents to defensive positions, protecting their authority or exercising it simply to show who is the boss. Disciple-making is a process that systematically moves people toward Christ and spiritual maturity; it is not a bunch of randomly disconnected church activities. In the context of church leadership, one of the greatest and most important applications of faith is to trust the Holy Spirit to work in and through those you are leading. Without confidence that the Holy Spirit is in control, there is no empowering, no shared leadership, and, as a consequence, no multiplication.

To imagine an end to caste in America, we need only look at the history of Germany. It is living proof that if a caste system—the twelve-year reign of the Nazis—can be created, it can be dismantled. We make a serious error when we fail to see the overlap between our country and others, the common vulnerability in human programming, what the political theorist Hannah Arendt called “the banality of evil.” “It’s all too easy to imagine that the Third Reich was a bizarre aberration,” wrote the philosopher David Livingstone Smith, who has studied cultures of dehumanization. “It’s tempting to imagine that the Germans were (or are) a uniquely cruel and bloodthirsty people. But these diagnoses are dangerously wrong. What’s most disturbing about the Nazi phenomenon is not that the Nazis were madmen or monsters. It’s that they were ordinary human beings.

For example, say you're an average web developer. You're familiar with a dozen programming languages, tons of helpful libraries, standards, protocols, what have you. You still have to learn more at the rate of about one a week, and remember to check the hundreds of things you know to see if they've been updated or broken and make sure they all still work together and that nobody fixed the bug in one of them that you exploited to do something you thought was really clever one weekend when you were drunk. You're all up to date, so that's cool, then everything breaks. "Double you tee eff?" you say, and start hunting for the problem. You discover that one day, some idiot decided that since another idiot decided that 1/0 should equal infinity, they could just use that as a shorthand for "Infinity" when simplifying their code. Then a non-idiot rightly decided that this was idiotic, which is what the original idiot should have decided, but since he didn't, the non-idiot decided to be a dick and make this a failing error in his new compiler. Then he decided he wasn't going to tell anyone that this was an error, because he's a dick, and now all your snowflakes are urine and you can't even find the cat.

Before we conclude that human cognitive mechanisms are riddled with biases and errors of judgment, we need to ask which adaptive problems human cognitive mechanisms evolved to solve and what would be “sound judgment” or “successful reasoning” from an evolutionary perspective. If humans have trouble locating their cars by color at night in parking lots illuminated with sodium vapor lamps, we would not conclude that our visual system is riddled with errors. Our eyes were designed to perceive the color of objects under natural, not artificial, light (Shepard, 1992). Many of the research programs that have documented “biases” in judgment, it turns out, have used artificial, evolutionarily unprecedented experimental stimuli that are analogous to sodium vapor lamps. Many, for example, require subjects to make probability judgments based on a single event (Gigerenzer, 1991, 1998). “Reliable numerical statements about the probability of a single event were rare or nonexistent in the Pleistocene—a conclusion reinforced by the relative poverty of number terms in modern band level societies” (Tooby & Cosmides, 1998 p. 40). A specific woman cannot have a 35 percent chance of being pregnant; she either is pregnant or is not, so probabilities hardly make sense when applied to a single case.

This happens because data scientists all too often lose sight of the folks on the receiving end of the transaction. They certainly understand that a data-crunching program is bound to misinterpret people a certain percentage of “he time, putting them in the wrong groups and denying them a job or a chance at their dream house. But as a rule, the people running the WMDs don’t dwell on those errors. Their feedback is money, which is also their incentive. Their systems are engineered to gobble up more data and fine-tune their analytics so that more money will pour in. Investors, of course, feast on these returns and shower WMD companies with more money. And the victims? Well, an internal data scientist might say, no statistical system can be perfect. Those folks are collateral damage. And often, like Sarah Wysocki, they are deemed unworthy and expendable. Big Data has plenty of evangelists, but I’m not one of them. This book will focus sharply in the other direction, on the damage inflicted by WMDs and the injustice they perpetuate. We will explore harmful examples that affect people at critical life moments: going to college, borrowing money, getting sentenced to prison, or finding and holding a job. All of these life domains are increasingly controlled by secret models wielding arbitrary punishments.

one stubborn glitch they couldn’t figure out: the program did a wonderful job spewing out data on the trajectory of artillery shells, but it just didn’t know when to stop. Even after the shell would have hit the ground, the program kept calculating its trajectory, “like a hypothetical shell burrowing through the ground at the same rate it had traveled through the air,” as Jennings described it. “Unless we solved that problem, we knew the demonstration would be a dud, and the ENIAC’s inventors and engineers would be embarrassed.”69 Jennings and Snyder worked late into the evening before the press briefing trying to fix it, but they couldn’t. They finally gave up at midnight, when Snyder needed to catch the last train to her suburban apartment. But after she went to bed, Snyder figured it out: “I woke up in the middle of the night thinking what that error was. . . . I came in, made a special trip on the early train that morning to look at a certain wire.” The problem was that there was a setting at the end of a “do loop” that was one digit off. She flipped the requisite switch and the glitch was fixed. “Betty could do more logical reasoning while she was asleep than most people can do awake,” Jennings later marveled. “While she slept, her subconscious untangled the knot that her conscious mind had been unable to.

The main ones are the symbolists, connectionists, evolutionaries, Bayesians, and analogizers. Each tribe has a set of core beliefs, and a particular problem that it cares most about. It has found a solution to that problem, based on ideas from its allied fields of science, and it has a master algorithm that embodies it. For symbolists, all intelligence can be reduced to manipulating symbols, in the same way that a mathematician solves equations by replacing expressions by other expressions. Symbolists understand that you can’t learn from scratch: you need some initial knowledge to go with the data. They’ve figured out how to incorporate preexisting knowledge into learning, and how to combine different pieces of knowledge on the fly in order to solve new problems. Their master algorithm is inverse deduction, which figures out what knowledge is missing in order to make a deduction go through, and then makes it as general as possible. For connectionists, learning is what the brain does, and so what we need to do is reverse engineer it. The brain learns by adjusting the strengths of connections between neurons, and the crucial problem is figuring out which connections are to blame for which errors and changing them accordingly. The connectionists’ master algorithm is backpropagation, which compares a system’s output with the desired one and then successively changes the connections in layer after layer of neurons so as to bring the output closer to what it should be. Evolutionaries believe that the mother of all learning is natural selection. If it made us, it can make anything, and all we need to do is simulate it on the computer. The key problem that evolutionaries solve is learning structure: not just adjusting parameters, like backpropagation does, but creating the brain that those adjustments can then fine-tune. The evolutionaries’ master algorithm is genetic programming, which mates and evolves computer programs in the same way that nature mates and evolves organisms. Bayesians are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete, and even contradictory information without falling apart. The solution is probabilistic inference, and the master algorithm is Bayes’ theorem and its derivates. Bayes’ theorem tells us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible. For analogizers, the key to learning is recognizing similarities between situations and thereby inferring other similarities. If two patients have similar symptoms, perhaps they have the same disease. The key problem is judging how similar two things are. The analogizers’ master algorithm is the support vector machine, which figures out which experiences to remember and how to combine them to make new predictions.

Click, hum, click, hum, click, hum. Click, click, click, click, click, hum. Hmmm. A low-level supervising program woke up a slightly higher-level supervising program deep in the ship’s semisomnolent cyberbrain and reported to it that whenever it went click all it got was a hum. The higher-level supervising program asked it what it was supposed to get, and the low-level supervising program said that it couldn’t remember what it was meant to get, exactly, but thought it was probably more of a sort of distant satisfied sigh, wasn’t it? It didn’t know what this hum was. Click, hum, click, hum. That was all it was getting. The higher-level supervising program considered this and didn’t like it. It asked the low-level supervising program what exactly it was supervising and the low-level supervising program said it couldn’t remember that either, just that it was something that was meant to go click, sigh every ten years or so, which usually happened without fail. It had tried to consult its error look-up table but couldn’t find it, which was why it had alerted the higher-level supervising program of the problem. The higher-level supervising program went to consult one of its own look-up tables to find out what the low-level supervising program was meant to be supervising. It couldn’t find the look-up table. Odd. It looked again. All it got was an error message. It tried to look up the error message in its error message look-up table and couldn’t find that either. It allowed a couple of nanoseconds to go by while it went through all this again. Then it woke up its sector function supervisor. The sector function supervisor hit immediate problems. It called its supervising agent, which hit problems too. Within a few millionths of a second virtual circuits that had lain dormant, some for years, some for centuries, were flaring into life throughout the ship. Something, somewhere, had gone terribly wrong, but none of the supervising programs could tell what it was. At every level, vital instructions were missing, and the instructions about what to do in the event of discovering that vital instructions were missing, were also missing. Small modules of software—agents—surged through the logical pathways, grouping, consulting, regrouping. They quickly established that the ship’s memory, all the way back to its central mission module, was in tatters. No amount of interrogation could determine what it was that had happened. Even the central mission module itself seemed to be damaged. This made the whole problem very simple to deal with, in fact. Replace the central mission module. There was another one, a backup, an exact duplicate of the original. It had to be physically replaced because, for safety reasons, there was no link whatsoever between the original and its backup. Once the central mission module was replaced it could itself supervise the reconstruction of the rest of the system in every detail, and all would be well.

Classical liberalism has been reproached with being too obstinate and not ready enough to compromise. It was because of its inflexibility that it was defeated in its struggle with the nascent anticapitalist parties of all kinds. If it had realized, as these other parties did, the importance of compromise and concession to popular slogans in winning the favor of the masses, it would have been able to preserve at least some of its influence. But it has never bothered to build for itself a party organization and a party machine as the anticapitalist parties have done. It has never attached any importance to political tactics in electoral campaigns and parliamentary proceedings. It has never gone in for scheming opportunism or political bargaining. This unyielding doctrinairism necessarily brought about the decline of liberalism. The factual assertions contained in these statements are entirely in accordance with the truth, but to believe that they constitute a reproach against liberalism is to reveal a complete misunderstanding of its essential spirit. The ultimate and most profound of the fundamental insights of liberal thought is that it is ideas that constitute the foundation on which the whole edifice of human social cooperation is Liberalism: A Socio-Economic Exposition constructed and sustained and that a lasting social structure cannot be built on the basis of false and mistaken ideas. Nothing can serve as a substitute for an ideology that enhances human life by fostering social cooperation—least of all lies, whether they be called "tactics," "diplomacy," or "compromise." If men will not, from a recognition of social necessity, voluntarily do what must be done if society is to be maintained and general well-being advanced, no one can lead them to the right path by any cunning stratagem or artifice. If they err and go astray, then one must endeavor to enlighten them by instruction. But if they cannot be enlightened, if they persist in error, then nothing can be done to prevent catastrophe. All the tricks and lies of demagogic politicians may well be suited to promote the cause of those who, whether in good faith or bad, work for the destruction of society. But the cause of social progress, the cause of the further development and intensification of social bonds, cannot be advanced by lies and demagogy. No power on earth, no crafty stratagem or clever deception could succeed in duping mankind into accepting a social doctrine that it not only does not acknowledge, but openly spurns. The only way open to anyone who wishes to lead the world back to liberalism is to convince his fellow citizens of the necessity of adopting the liberal program. This work of enlightenment is the sole task that the liberal can and must perform in order to avert as much as lies within his power the destruction toward which society is rapidly heading today. There is no place here for concessions to any of the favorite or customary prejudices and errors. In regard to questions that will decide whether or not society is to continue to exist at all, whether millions of people are to prosper or perish, there is no room for compromise either from weakness or from misplaced deference for the sensibilities of others. If liberal principles once again are allowed to guide the policies of great nations, if a revolution in public opinion could once more give capitalism free rein, the world will be able gradually to raise itself from the condition into which the policies of the combined anticapitalist factions have plunged it. There is no other way out of the political and social chaos of the present age.

The greeting of risk, the willingness to discover through (certain classes of non-lethal) trial and error, the subordination of success to exploration and discovery, and the insistence of finding the edge of patterns; where they fail, all of these seem to contain echoes of field work in Special Forces and related intelligence organizations, the passion for languages, the recognition that much of what passes for effective communication can be achieved with very little actual understanding, the primacy of non-verbal communication in influencing face-to-face communications, a tolerance for ambiguity and vagueness, and a fascination with the unknown.

Quoting page 65-66: Race-conscious affirmative action is a familiar term of journalistic convenience. It identifies unambiguously the controversial element of minority preferences in distributing benefits. But it also conflates racially targeted civil rights remedies with affirmative action preferences for groups, such as Hispanics and women, given protected class status irrespective of race. … It includes nonracial as well as racial preferences, and it distinguishes such remedies, available only to officially designated protected classes, from the soft affirmative action … which emphasized special outreach programs for recruiting minorities … within a traditional liberal framework of equal individual rights for all Americans. … The architects of race-conscious affirmative action, Skrentny observes, developed their remedy in the face of public opinion heavily arrayed against it. Unlike most public policy in America, hard affirmative action was originally adopted without the benefit of any organized lobbying by the major interest groups involved. Instead, government bureaucrats, not benefiting interest groups, provided the main impetus. The race-conscious model of hard affirmative action was developed in trial-and-error fashion by a coalition of mostly white, second-tier civil servants in the social service agencies of the presidency… To Skrenty’s core irony, we may add three further ironies, first, the key to political survival for hard affirmative action was persistent support from the Republican Party… Second, the theories of compensatory justice supporting minority preference policies were devised only after the adoption of the policies themselves. Finally, affirmative action preferences which supporters rationalized as necessary to compensate African-Americans for historic discrimination, and which for twenty years were successfully defended in federal courts primarily on those grounds, soon benefited millions of immigrants newly arrived from Latin America and Asia.

Normally, when you forget to put let in front of your binding, as with counter in the example, JavaScript quietly creates a global binding and uses that. In strict mode, an error is reported instead.

Another problem with callbacks is that they can make handling errors difficult. If an asynchronous function (or an asynchronously invoked callback) throws an exception, there is no way for that exception to propagate back to the initiator of the asynchronous operation.

The progressive Christian’s error lies in believing that Christianity’s perennial polemic against the rich is an implicit defense of socialist programs.

How come everyone else could function and I couldn't? How could everyone live properly, yet I had some sort of error in my programming?

Above all, science cannot dispel religion by showing it to be an illusion. The rationalist philosophy according to which religion is an intellectual error is fundamentally at odds with scientific inquiry into religion as a natural human activity. Religion may involve the creation of illusions. But there is nothing in science that says illusion may not be useful, even indispensable, in life. The human mind is programmed for survival, not truth. Rather than producing minds that see the world ever more clearly, evolution could have the effect of breeding any clear view of things out of the mind. The upshot of scientific inquiry could be that a need for illusion goes with being human. The recurring appearance of religions of science suggests this may in fact be the case.

Now, there are other themes arising from our blindness to the Black Swan: We focus on preselected segments of the seen and generalize from it to the unseen: the error of confirmation. We fool ourselves with stories that cater to our Platonic thirst for distinct patterns: the narrative fallacy. We behave as if the Black Swan does not exist: human nature is not programmed for Black Swans. What we see is not necessarily all that is there. History hides Black Swans from us and gives us a mistaken idea about the odds of these events: this is the distortion of silent evidence.

Now, there are other themes arising from our blindness to the Black Swan: We focus on preselected segments of the seen and generalize from it to the unseen: the error of confirmation. We fool ourselves with stories that cater to our Platonic thirst for distinct patterns: the narrative fallacy. We behave as if the Black Swan does not exist: human nature is not programmed for Black Swans. What we see is not necessarily all that is there. History hides Black Swans from us and gives us a mistaken idea about the odds of these events: this is the distortion of silent evidence. We “tunnel”: that is, we focus on a few well-defined sources of uncertainty, on too specific a list of Black Swans (at the expense of the others that do not easily come to mind).

A programmer who truly sees his program as an extension of his own ego is not going to be trying to find all the errors in that program. On the contrary, he is going to be trying to prove that the program is correct—even if this means the oversight of errors which are monstrous to another eye. All programmers are familiar with the symptoms of this dissonance resolution—-in others, of course.

Overconfidence by the programmer could be attacked by a system that introduced random errors into the program under test. The location and nature of these errors would be recorded inside the system but concealed from the programmer. The rate at which he found and removed these known errors could be used to estimate the rate at which he is removing unknown errors. A similar technique is used routinely by surveillance systems in which an operator is expected to spend eight hours at a stretch looking at a radar screen for very rare events—such as the passing of an unidentified aircraft. Tests of performance showed that it was necessary to introduce some nonzero rate of occurrence of artificial events in order to keep the operator in a satisfactory state of arousal. Moreover, since these events were under control of the system, it was able to estimate the current and overall performance of each operator.

Often, no matter how careful you tried to be, sheer exhaustion would lead to errors that weren’t caught until it was too late. Sometimes it was due to what we called the F9 mistake. Back then, computers were very slow, so you didn’t want to wait for the spreadsheet program to recalculate automatically every time you made a change. You would instead turn off that feature, but then you needed to be careful to remember to hit F9 at the end, which would trigger the recalculation of data throughout the model. There were always stories about analysts who made a bunch of changes and then forgot to hit F9, printing the books with faulty numbers. They might realize during the client presentation, or perhaps after the meeting, that the wrong data had been utilized. The models were so complicated that usually no one would notice, but people were making big decisions based on erroneous information. How many deals were done, we wondered, or people laid off because some sleep-deprived analyst got a model wrong? Steve forgot to hit F9; ten thousand people got fired.

What happened?” Richard asked. “We put a drone up in the air, or rather, Mark put it up in the air. He’s flown them before with no trouble, but this time it crashed and burned.” Which, Richard thought, is exactly what I programmed it to do: take off, fly flawlessly for a while, and then drop out of the sky for no apparent reason.

Compare it to a new computer that's running ten heavy programs at once. It gets slower and slower, making errors, and eventually it freezes - not because it's a bad computer, but because it has to do too much at once. Poor people have an analogous problem. They're not making dumb decisions because they are dumb, but because they're living in a context in which anyone would make dumb decisions.

the executive director of the WHO emergencies program, Dr. Michael Ryan, put his own spin on this observation. “Perfection is the enemy of the good when it comes to emergency management,” he said. “Speed trumps perfection, and the problem in society we have at the moment is everyone is afraid of making a mistake, everyone is afraid of the consequence of error. But the greatest error is not to move. The greatest error is to be paralyzed by the fear of failure.

QUESTIONS TO CONSIDER Are your people trying to achieve excellence or just to avoid making mistakes? Has your organization become action-averse because taking action sometimes results in errors? Have you let error-reduction programs sap the lifeblood out of initiative and risk taking? Do you spend more time critiquing errors than celebrating success? Are you able to identify the symptoms of avoiding errors in your workplace? When you ask people what their jobs are, do they answer in terms of reducing errors?

In May 1985 the historic paper was published that announced an “ozone hole” in the Southern Hemisphere.11 The news shocked the scientific world. If true, the results proved that humankind had already exceeded a global limit. CFC use had grown above sustainable limits. Humans were already in the process of destroying their ozone shield. Scientists at the National Aeronautics and Space Administration of the United States (NASA) scrambled to check readings on atmospheric ozone made by the Nimbus 7 satellite, measurements that had been taken routinely since 1978. Nimbus 7 had never indicated an ozone hole. Checking back, NASA scientists found that their computers had been programmed to reject very low ozone readings on the assumption that such low readings must indicate instrument error.12 Fortunately the measurements thrown out by the computer were recoverable. They confirmed the Halley Bay observations, showing that ozone levels had been dropping over the South Pole for a decade. Furthermore, they provided a detailed map of the hole in the ozone layer. It was enormous, about the size of the continental United States, and it had been getting larger and deeper every year.

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How come everyone else could function and I couldn't? How could everyone live properly yet I had some sort of error in my programming?

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I think it’s extraordinarily important that we in computer science keep fun in computing. When it started out, it was an awful lot of fun. Of course, the paying customers got shafted every now and then, and after a while we began to take their complaints seriously. We began to feel as though we really were responsible for the successful, error-free, perfect use of these machines. I don’t think we are. I think we’re responsible for stretching them, setting them off in new directions, and keeping fun in the house. Fun comes in many ways. Fun comes in making a discovery, proving a theorem, writing a program, breaking a code. Whatever form or sense it comes in I hope the field of computer science never loses its sense of fun. Above all, I hope we don’t become missionaries. What you know about computing other people will learn. Don’t feel as though the key to successful computing is only in your hands. What’s in your hands, I think and hope, is intelligence: the ability to see the machine as more than when you were first led up to it, that you can make it more.

The progressive Christian’s error lies in believing that Christianity’s perennial polemic against the rich is an implicit defense of socialist programs. What concerns the Christ of the Gospels is not the economic situation of the poor man, but the moral condition of the rich man.

The organizations of fast innovators are structured for ease of coordination and speed of execution. Slow innovators are structured for functional control, cost efficiency, and risk avoidance, a structure that results in a slow and cumbersome development process. With the functional organization come multiple hand-offs that consume time, cause errors to occur and diminish overall accountability. Coordination and control can only be accomplished through elaborate review processes and documentation requirements. Quality seems to decline, not improve. Functions have their firm budgets and manage themselves to their budgets even at the expense of time. To help ensure that budgets are met, performance targets are conservative. Senior management actively participates in program decisions, and, because of their very full schedules, further slow the decision-making processes of the company.

Errors in code are what programmers call bugs, though when our programs go wrong, we prefer to call them “unexpected additional features.” Very

pdb also includes a postmortem function (pdb.pm()) that you can run after an exception occurs, to get information from the time of the error. See

Writing and repairing software generally takes far more time and is far more expensive than initially anticipated. “Every feature that is added and every bug that is fixed,” Edward Tenner points out, “adds the possibility of some new and unexpected interaction between parts of the program.”19 De Jager concurs: “If people have learned anything about large software projects, it is that many of them miss their deadlines, and those that are on time seldom work perfectly. … Indeed, on-time error-free installations of complex computer systems are rare.”20 Even small changes to code can require wholesale retesting of entire software systems. While at MIT in the 1980s, I helped develop some moderately complex software. I learned then that the biggest problems arise from bugs that creep into programs during early stages of design. They become deeply embedded in the software’s interdependent network of logic, and if left unfixed can have cascading repercussions throughout the software. But fixing them often requires tracing out consequences that have metastasized in every direction from the original error. As the amount of computer code in our world soars (doubling every two years in consumer products alone), we need practical ways to minimize the number of bugs. But software development is still at a preindustrial stage—it remains more craft than engineering. Programmers resemble artisans: they handcraft computer code out of basic programming languages using logic, intuition, and pattern-recognition skills honed over years of experience.

The five most highly correlated factors are: Organizational culture. Strong feelings of burnout are found in organizations with a pathological, power-oriented culture. Managers are ultimately responsible for fostering a supportive and respectful work environment, and they can do so by creating a blame-free environment, striving to learn from failures, and communicating a shared sense of purpose. Managers should also watch for other contributing factors and remember that human error is never the root cause of failure in systems. Deployment pain. Complex, painful deployments that must be performed outside of business hours contribute to high stress and feelings of lack of control.4 With the right practices in place, deployments don’t have to be painful events. Managers and leaders should ask their teams how painful their deployments are and fix the things that hurt the most. Effectiveness of leaders. Responsibilities of a team leader include limiting work in process and eliminating roadblocks for the team so they can get their work done. It’s not surprising that respondents with effective team leaders reported lower levels of burnout. Organizational investments in DevOps. Organizations that invest in developing the skills and capabilities of their teams get better outcomes. Investing in training and providing people with the necessary support and resources (including time) to acquire new skills are critical to the successful adoption of DevOps. Organizational performance. Our data shows that Lean management and continuous delivery practices help improve software delivery performance, which in turn improves organizational performance. At the heart of Lean management is giving employees the necessary time and resources to improve their own work. This means creating a work environment that supports experimentation, failure, and learning, and allows employees to make decisions that affect their jobs. This also means creating space for employees to do new, creative, value-add work during the work week—and not just expecting them to devote extra time after hours. A good example of this is Google’s 20% time policy, where the company allows employees 20% of their week to work on new projects, or IBM’s “THINK Friday” program, where Friday afternoons are designated for time without meetings and employees are encouraged to work on new and exciting projects they normally don’t have time for.

In summary, never return null in place of an empty array or collection. It makes your API more difficult to use and more prone to error, and it has no performance advantages.

I do not believe we will find the magic here. Program verification is a very powerful concept, and it will be very important for such things as secure operating system kernels. The technology does not promise, however, to save labor. Verifications are so much work that only a few substantial programs have ever been verified. Program verification does not mean error-proof programs. There is no magic here, either. Mathematical proofs also can be faulty. So whereas verification might reduce the program-testing load, it cannot eliminate it. More seriously, even perfect program verification can only establish that a program meets its specification. The hardest part of the software task is arriving at a complete and consistent specification, and much of the essence of building a program is in fact the debugging of the specification.

Sir Roy Fedden headed the British team sent to defeated Germany by Sir Stafford Cripps. Fedden, a slim, elegant, clean-shaven man whose photographs usually reveal an expression of focused determination, showed keen intelligence and a fascination with car and aircraft engines at an early age. Passionately fond of his wife Norah Crew, and somehow finding time between engine experiments to sail and fish, Fedden, 60 years of age in 1945, attacked his task with customary gusto. Fedden Years earlier, Erhardt Milch and Hermann Goering, to Fedden's astonishment, permitted him to tour no less than 17 of their secret aeronautics facilities when he visited Germany in 1937 and 1938. The Luftwaffe leaders hoped to overawe Fedden with the potential of German military aircraft design, and thus cause him to influence the British government to reach an accommodation with the Third Reich. Fedden, in fact, urged the English leadership to modernize their aircraft design to match the Germans' potential and was fired.               Realizing their error several years later, the government re-employed Fedden in 1944, and a mix of aeronautics engineers, scientists, and RAF officers comprised Fedden's team.

Go does not permit unused local variables, so this would result in a compilation error.

That night was like the first computer program he ever wrote: a series of run-time errors followed by a quick compilation.

If you tried to assign any different value to TAX_RATE after that first declaration, your program would reject the change (and in strict mode, fail with an error — see “Strict Mode” in Chapter 2 ).

And so we have gone back over the television we recall watching before we gave it up entirely and made a list of those series that never crossed the line into immorality. The list is in the empty box above. Actually that is an exaggeration, but the list is rather short. Antiques Roadshow has never crossed the line in the episodes I watched. Also Extreme Homes never crossed the line. As I recall This Old House didn't. Although I was an avid viewer of Jeopardy it did cross the line by spreading false propaganda. True, I would sit there and correct them, but one cannot recommend a program that habitually teaches error. Our minds are created for truth and we should only allow the truth to enter our minds.

By the end of the meeting, one conscientious human being had cleared up the confusion generated by web-crawling data-gathering programs. The housing authority knew which Catherine Taylor it was dealing with. The question we’re left with is this: How many Wanda Taylors are out there clearing up false identities and other errors in our data? The answer: not nearly enough. Humans in the data economy are outliers and throwbacks.

lack drama. Why is it that we reward programmers who work all night to remove the errors they put into their programs, or managers who make drastic organizational changes to resolve the crises their poor management has created? Why not reward the programmers who design so well that they don’t have dramatic errors, and managers whose organizations stay out of crisis mode? Organizing

Thoughtful design, code review, pair programming, and a considered test strategy (including TDD practices and fully automated unit test suites) are all of the utmost importance. Techniques like assertions, defensive programming, and code coverage tools will all help minimise the likelihood of errors sneaking past.  We all know these mantras. Don’t we?

Today 60% of workers in the private sector receive their insurance through their employer. I believe that by 2025 fewer than 20% of workers at private companies will continue to receive their health insurance through an employer-sponsored program. Nevertheless many will still receive an employer contribution, a so-called defined contribution toward the purchase of health insurance in the exchange. I believe the majority of private-sector workers will get their coverage through the exchanges.

debugger, a tool that will preserve your C source code after compilation and enable you to do such things as step through it manually, or alter data as an aid to finding and correcting programming errors.

There are only 2 hard things in programming: naming, cache invalidation, and off-by-one-errors.

Coming from the field of aviation, where mundane errors could cost lives, he was exact. For all these reasons, Dunie was drawn to testing, the grubby and anonymous side of programming.

When the ANSI C standard was under development, the pragma directive was introduced. Borrowed from Ada, #pragma is used to convey hints to the compiler, such as the desire to expand a particular function in-line or suppress range checks. Not previously seen in C, pragma met with some initial resistance from a gcc implementor, who took the “implementation-defined” effect very literally—in gcc version 1.34, the use of pragma causes the compiler to stop compiling and launch a computer game instead! The gcc manual contained the following: The “#pragma” command is specified in the ANSI standard to have an arbitrary implementation-defined effect. In the GNU C preprocessor, “#pragma” first attempts to run the game “rogue”; if that fails, it tries to run the game “hack”; if that fails, it tries to run GNU Emacs displaying the Tower of Hanoi; if that fails, it reports a fatal error. In any case, preprocessing does not continue. —Manual for version 1.34 of the GNU C compiler

Human beings are weak, prone to errors and superficiality. Our programming eradicates such frailties and there is great freedom in our state of existence.

Defensive running strategies thus become an important part of your training plan. If you want to have a long running career, determine what activities most often cause you to become injured, then avoid them. This is particularly true when it comes to overuse injuries that result from what might be called training errors.

Rushing to optimize before the bottlenecks are known may be the only error to have ruined more designs than feature creep. From tortured code to incomprehensible data layouts, the results of obsessing about speed or memory or disk usage at the expense of transparency and simplicity are everywhere. They spawn innumerable bugs and cost millions of man-hours—often, just to get marginal gains in the use of some resource much less expensive than debugging time. Disturbingly often, premature local optimization actually hinders global optimization (and hence reduces overall performance). A prematurely optimized portion of a design frequently interferes with changes that would have much higher payoffs across the whole design, so you end up with both inferior performance and excessively complex code.

these people have computing power that will be able to crack even AES encryption in a matter of hours. Our government has made it difficult in the extreme to legally distribute programs that will encrypt even to that level, and our cipher is nowhere near that powerful. All they would have to do is read one of our messages and then perform trial and error runs on computers that would put a Cray supercomputer to shame. It would take days, perhaps, but not a week.” “So you think we should make ourselves scarce until you find out more.” “Yes, I do. What makes it even more alarming, is that if it is our government that is after you, they could easily make you disappear, but recent developments mean that even more nefarious groups may know of the government’s interest.

Programs written in dynamically typed languages require large suites of tests to give some assurance that simple type errors cannot occur.

People write programs without any expectation that they will be right the first time. They spend at least as much time testing and correcting errors as they spent writing the initial program. ... Software is released for use, not when it is known to be correct, but when the rate of discovering errors slows down to one that management considers acceptable.

In addition to the habituation and education of programmers are the constraints offered by the programming environments themselves which can be very unforgiving. Punctuation, for example, is part of the syntax of programming languages and misplaced punctuation can cause all sorts of strange bugs and errors to occur

A good example is overflow detection on arithmetic, or providing bignums instead of just letting 32-bit integers wrap around. Now, implementing those is more expensive but I believe that providing full-blown bignums is a little less error-prone for some kinds of programming. A trap that I find systems programmers and designers of operating-systems algorithms constantly falling into is they say, "Well, we need to synchronize some phases here so we're going to use a take-a-number strategy. Every time we enter a new phase of the computation we'll increment some variable and that'll be the new number and then the different participants will make sure they're all working on the same phase number before a certain operation happens." And that works pretty well in practice, but if you use a 32-bit integer it doesn't take that long to count to four billion anymore. What happens if that number wraps around? Will you still be OK or not? It turns out that a lot of such algorithms in the literature have that lurking bug. What if some thread stalls for 2 to the 32nd iterations? That's highly unlikely in practice, but it's a possibility. And one should either mitigate that correctness problem or else do the calculation to show that, yeah, it's sufficiently unlikely that I don't want to worry about it. Or maybe you're willing to accept one glitch every day. But the point is you should do the analysis rather than simply ignoring the issue. And the fact that counters can wrap around is a lurking pitfall

From the Bridge” by Captain Hank Bracker Pebbles, Rocks & Mountains Rocks can be formed in many different ways and are found in just about every corner of our planet, the Moon, up in space and who knows where else. Now pebbles are the mini-me’s of rocks and generally are about one to three inches in size. Geologists will tell you that they are about 5 millimeters in diameter, but who’s counting? In fact there are two beaches that are made up entirely of pebbles such as the Shingle Beach in Somerset, England. Generally pebbles are found along rivers, streams and creeks whereas mountains are usually a part of a chain that was created along geothermal fault lines. The process of Mountain formation is associated with movements of the earth's crust, which is referred to as plate tectonics. See; now that I looked it up, I know these things! What I’m about to say has absolutely nothing to do with geology and everything to do about human nature. In the course of events we never trip over mountains and seldom over rocks, but tripping over pebbles is another thing. Marilyn French, a writer and feminist scholar is credited with saying, “Men (she should have included Women) stumble over pebbles, never over mountains.” She was the lady (I should have said woman) whose provocative 1977 novel, “The Women's Room” captured the frustration and fury of a generation of women fed up with society's traditional conceptions of their roles (and this is true). However, this has nothing to do with the feminist movement and is simply a metaphor. Of course we’re not going to trip over mountains, not unless we are bigger than the “Jolly Green Giant!” and so it’s usually the little things that trip us up and cause us problems. What comes to mind is found on page 466 of The Exciting Story of Cuba. This is a book that won two awards by the “Florida Authors & Publishers Association” and yet there are small mistakes. They weren’t even caused by me or my team and yet there they are, getting bigger and bigger every time I look at them. Now I’m not about to tell you what they are, since that would take the fun out of it, but if you look hard enough in the book, you’ll succeed in discovering them! I will however tell you that one of these mistakes was caused by a computer program called “Word.” It’s wonderful that this program has a spell check and can even correct my grammar, but it can’t read my mind. In its infernal wisdom, the program was so insistent that it was right and that I was wrong that it changed the spelling of, in this case, the name of a person in the middle of the night. It happened while I was sleeping! I would have seen it if it had been as big as a mountain, however being just a little pebble it escaped my review and even escaped the eagle eyes of Lucy who still remains the best proof reader and copy editor that I know. When you discover what I missed please refrain from emailing me, although, normally, I would really enjoy hearing from you! I unfortunately already know most of the errors in the book, for which I take full responsibility. The truth of it is that my mistakes leave me feeling stupid and frustrated. Now, you may disagree with me however I don’t think that I am really all that stupid, but when you write hundreds of thousands of words, a few of them might just slip between the cracks. None of us are infallible and we all make mistakes. I sometimes like to say that “I once thought that I had made a mistake, but then found out that I was mistaken.” And so it is; if you think about it, it’s the pebbles that create most of our problems, not the rocks and certainly not the mountains. I’ll let you know as soon as my other books, Suppressed I Rise – Revised Edition; Seawater One…. And Words of Wisdom, “From the Bridge” are available. It’s Seawater One that has the naughty bits in it… but that just spices it up. Now with that book you can really tell me what you think….

Punishing potty errors after they’ve happened is perhaps the most common training mistake; it only aggravates the problem. Going

The only way to deal with a potty error is to catch the dog in the act and scoop her up and take her, or lead her, quickly outside to the potty area. Until