Computational survivalist

Some programmers and systems engineers try to do everything they can with basic command line tools on the grounds that someday they may be in an environment where that’s all they have. I think of this as a sort of computational survivalism.

I’m not much of a computational survivalist, but I’ve come to appreciate such a perspective. It’s an efficiency/robustness trade-off, and in general I’ve come to appreciate the robustness side of such trade-offs more over time. It especially makes sense for consultants who find themselves working on someone else’s computer with no ability to install software. I’m rarely in that position, but that’s kinda where I am on one project.

Example

I’m working on a project where all my work has to be done on the client’s laptop, and the laptop is locked down for security. I can’t install anything. I can request to have software installed, but it takes a long time to get approval. It’s a Windows box, and I requested a set of ports of basic Unix utilities at the beginning of the project, not knowing what I might need them for. That has turned out to be a fortunate choice on several occasions.

For example, today I needed to count how many times certain characters appear in a large text file. My first instinct was to write a Python script, but I don’t have Python. My next idea was to use grep -c, but that would count the number of lines containing a given character, not the number of occurrences of the character per se.

I did a quick search and found a Stack Overflow question “How can I use the UNIX shell to count the number of times a letter appears in a text file?” On the nose! The top answer said to use grep -o and pipe it to wc -l.

The -o option tells grep to output the regex matches, one per line. So counting the number of lines with wc -l gives the number of matches.

Computational minimalism

Computational minimalism is a variation on computational survivalism. Computational minimalists limit themselves to a small set of tools, maybe the same set of tools as computational survivalist, but for different reasons.

I’m more sympathetic to minimalism than survivalism. You can be more productive by learning to use a small set of tools well than by hacking away with a large set of tools you hardly know how to use. I use a lot of different applications, but not as many as I once used.

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Professional, amateur, and something else

I opened a blog posts a while back by saying

One of the differences between amateur and professional software development is whether you’re writing software for yourself or for someone else. It’s like the difference between keeping a journal and being a journalist.

This morning I saw where someone pulled that quote and I thought about how I’m now somewhere that doesn’t fall into either category. I’m a professional, and a software developer, but not a professional software developer.

People pay me for work that may require writing software, but they usually don’t want my software per se. They want reports that may require me to write software to generate. My code is directly for my own use but indirectly for someone else. Occasionally I will have a client ask me for software, but not often.

In the last few years I’ve been asked to read software more often than I’ve been asked to write it. For example, I was an expert witness on an intellectual property case and had to read a lot of source code. But even that project fit into the pattern above: I wrote some code to help me do my review, but my client didn’t care about that code per se, only what I found with it.

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Curry-Howard-Lambek correspondence

Curry-Howard-Lambek is a set of correspondences between logic, programming, and category theory. You may have heard of the slogan proofs-as-programs or propositions-as-types. These refer to the Curry-Howard correspondence between mathematical proofs and programs. Lambek’s name is appended to the Curry-Howard correspondence to represent connections to category theory.

The term Curry-Howard isomorphism is often used but is an overstatement. Logic and programming are not isomorphic, nor is either isomorphic to category theory.

You’ll also hear the term computational trinitarianism. That may be appropriate because logic, programming, and category theory share common attributes without being identical.

The relations between logic, programming, and categories are more than analogies, but less than isomorphisms.

There are formal correspondences between parts of each theory. And aside from these rigorous connections, there is also a heuristic that something interesting in one area is likely to have an interesting counterpart in the other areas. In that sense Curry-Howard-Lambek is similar to the Langlands program relating number theory and geometry, a set of theorems and conjectures as well as a sort of philosophy.

Software quality: better in practice than in theory

Sir Tony Hoare

C. A. R. Hoare wrote an article How Did Software Get So Reliable Without Proof? in 1996 that still sounds contemporary for the most part.

In the 1980’s many believed that programs could not get much bigger unless we started using formal proof methods. The argument was that bugs are fairly common, and that each bug has the potential to bring a system down. Therefore the only way to build much larger systems was to rely on formal methods to catch bugs. And yet programs continued to get larger and formal methods never caught on. Hoare asks

Why have twenty years of pessimistic predictions been falsified?

Another twenty years later we can ask the same question. Systems have gotten far larger, and formal methods have not become common. Formal methods are used—more on that shortly—but have not become common.

Better in practice than in theory

It’s interesting that Hoare was the one to write this paper. He is best known for the quicksort, a sorting algorithm that works better in practice than in theory! Quicksort is commonly used in practice, even though has terrible worst-case efficiency, because its average efficiency has optimal asymptotic order [1], and in practice it works better than other algorithms with the same asymptotic order.

Economic considerations

It is logically possible that the smallest bug could bring down a system. And there have been examples, such as the Mars Climate Orbiter, where a single bug did in fact lead to complete failure. But this is rare. Most bugs are inconsequential.

Some will object “How can you be so blasé about bugs? A bug crashed a $300 million probe!” But what is the realistic alternative? Would spending an additional billion dollars on formal software verification have prevented the crash? Possibly, though not certainly, and the same money could send three more missions to Mars. (More along these lines here.)

It’s all a matter of economics. Formal verification is extremely tedious and expensive. The expense is worth it in some settings and not in others. The software that runs pacemakers is more critical than the software that runs a video game. For most software development, less formal methods have proved more cost effective at achieving acceptable quality: code reviews, unit tests, integration testing, etc.

Formal verification

I have some experience with formal software verification, including formal methods software used by NASA. When someone says that software has been formally verified, there’s an implicit disclaimer. It’s usually the algorithms have been formally verified, not the implementation of those algorithms in software. Also, maybe not all the algorithms have been verified, but say 90%, the remaining 10% being too difficult to verify. In any case, formally verified software can and has failed. Formal verification greatly reduces the probability of encountering a bug, but it does not reduce the probability to zero.

There has been a small resurgence of interest in formal methods since Hoare wrote his paper. And again, it’s all about economics. Theorem proving technology has improved over the last 20 years. And software is being used in contexts where the consequences of failure are high. But for most software, the most economical way to achieve acceptable quality is not through theorem proving.

There are also degrees of formality. Full theorem proving is extraordinarily tedious. If I remember correctly, one research group said that they could formally verify about one page of a mathematics textbook per man-week. But there’s a continuum between full formality and no formality. For example, you could have formal assurance that your software satisfies certain conditions, even if you can’t formally prove that the software is completely correct. Where you want to be along this continuum of formality is again a matter of economics. It depends on the probability and consequences of errors, and the cost of reducing these probabilities.

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[1] The worst-case performance of quicksort is O(n²) but the average performance is O(n log n).

Photograph of C. A. R. Hoare by Rama, Wikimedia Commons, Cc-by-sa-2.0-fr, CC BY-SA 2.0 fr

Does computer science help you program?

The relationship between programming and computer science is hard to describe. Purists will say that computer science has nothing to do with programming, but that goes too far.

Computer science is about more than programming, but it’s is all motivated by getting computers to do things. With few exceptions. students major in computer science in college with the intention of becoming programmers.

I asked on Twitter yesterday how helpful people found computer science in writing software.

In a follow up tweet I said “For this poll, basic CS would be data structures and analysis of algorithms. Advanced CS is anything after that.”

So about a quarter didn’t find computer science useful, but the rest either expected it to be useful or at least found the basic theory useful.

I suspect some of those who said they haven’t found (advanced) CS theory useful don’t know (advanced) CS theory. This isn’t a knock on them. It’s only the observation that you can’t use what you aren’t at least aware of. In fact, you may need to know something quite well before you can recognize an opportunity to use it. (More on that here.)

Many programmers are in jobs where they don’t have much need for computer science theory. I thought about making that a possible choice, something like “No, but I wish I were in a job that could use more theory.” Unfortunately Twitter survey responses have to be fairly short.

Of course this isn’t a scientific survey. (Even supposedly scientific surveys aren’t that great.) People who follow the CompSciFact twitter account have an interest in computer science. Maybe people who had strong feelings about CS, such as resentment for having to study something they didn’t want to or excitement for being able to use something they find interesting, were more likely to answer the question.

 

Proving life exists on Earth

NASA’s Galileo mission was primarily designed to explore Jupiter and its moons. In 1989, the Galileo probe started out traveling away from Jupiter in order to do a gravity assist swing around Venus. About a year later it also did a gravity assist maneuver around Earth. Carl Sagan suggested that when passing Earth, the Galileo probe should turn its sensors on Earth to look for signs of life. [1]

Artist conception of Galileo probe surveying Jupiter and its moons

Now obviously we know there’s life on Earth. But if we’re going look for life on other planets, it’s reasonable to ask that our methods return positive results when examining the one planet we know for sure does host life. So scientists looked at the data from Galileo as if it were coming from another planet to see what patterns in the data might indicate life.

I’ve started using looking for life on Earth as a metaphor. I’m working on a project right now where I’m looking for a needle in a haystack, or rather another needle in a haystack: I knew that one needle existed before I got started. So I want to make sure that my search procedure at least finds the one positive result I already know exists. I explained to a colleague that we need to make sure we can at least find life on Earth.

This reminds me of simulation work. You make up a scenario and treat it as the state of nature. Then pretend you don’t know that state, and see how successful your method is at discovering that state. It’s sort of a schizophrenic way of thinking, pretending that half of your brain doesn’t know what the other half is doing.

It also reminds me of software testing. The most trivial tests can be surprisingly effective at finding bugs. So you write a few tests to confirm that there’s life on Earth.

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[1] I found out about Galileo’s Earth reconnaissance listening to the latest episode of the .NET Rocks! podcast.

Iterating between theory and code

Yesterday I said on Twitter “Time to see whether practice agrees with theory, moving from LaTeX to Python. Wish me luck.” I got a lot of responses to that because it describes the experience of a lot of people. Someone asked if I’d blog about this. The content is confidential, but I’ll talk about the process. It’s a common pattern.

I’m writing code based on a theoretical result in a journal article.

First the program gets absurd results. Theory pinpoints a bug in the code.

Then the code confirms my suspicions of an error in the paper.

The code also uncovers, not an error per se, but an important missing detail in the paper.

Then code and theory differ by about 1%. Uncertain whether this is theoretical approximation error or a subtle bug.

Then try the code for different arguments, ones which theory predicts will have less approximation error, and now code and theory differ by 0.1%.

Then I have more confidence in (my understanding of) the theory and in my code.

The most disliked programming language

According to this post from Stack Overflow, Perl is the most disliked programming language.

I have fond memories of writing Perl, though it’s been a long time since I used it. I mostly wrote scripts for file munging, the task it does best, and never had to maintain someone else’s Perl code. Under different circumstances I probably would have had less favorable memories.

Perl is a very large, expressive language. That’s a positive if you’re working alone but a negative if working with others. Individuals can carve out their favorite subsets of Perl and ignore the rest, but two people may carve out different subsets. You may personally avoid some feature, but you have to learn it anyway if your colleague uses it. Also, in a large language there’s greater chance that you’ll accidentally use a feature you didn’t intend to. For example, in Perl you might use an array in a scalar context. This works, but not as you’d expect if you didn’t intend to do it.

I suspect that people who like large languages like C++ and Common Lisp are more inclined to like Perl, while people who prefer small languages like C and Scheme have opposite inclinations.

Related posts:

Programming language life expectancy

The Lindy effect says that what’s been around the longest is likely to remain around the longest. It applies to creative artifacts, not living things. A puppy is likely to live longer than an elderly dog, but a book that has been in press for a century is likely to be in press for another century.

In a previous post I go into the mathematical detail of the Lindy effect: power law distributions etc. The key fact we need for this blog post is that if something has the kind of survival distribution described by the Lindy effect, then the expected future lifetime equals the current age. For example, the 100 year old book in the opening paragraph is expected to be around for another 100 years.

Note that this is all framed in terms of probability distributions. It’s not saying, for example, that everything new will go away soon. Everything was once new. Someone watching Hamlet on opening night would be right to speculate that nobody would care about Hamlet in a few years. But now that we know Hamlet has been around for four centuries and is going strong, the Lindy effect would predict that people will be performing Hamlet in the 25th century.

Note that Lindy takes nothing into account except survival to date. Someone might have been more bullish on Hamlet after opening night based on other information such as how well the play was received, but that’s beyond the scope of the Lindy effect.

If we apply the Lindy effect to programming languages, we only consider how long they’ve been around and whether they’re thriving today. You might think that Go, for example, will be along for a long time based on Google’s enormous influence, but the Lindy effect does not take such information into account.

So, if we assume the Lindy effect holds, here are the expected years when programming languages would die. (How exactly would you define the time of death for a programming language? Doesn’t really matter. This isn’t that precise or that serious.)

Language Born Expected death
Go 2009 2025
C# 2000 2034
Java 1995 2039
Python 1991 2043
Haskell 1990 2044
C 1972 2062
Lisp 1959 2075
Fortran 1957 2077

You can debate what it even means for a language to survive. For example, I’d consider Lisp to be alive and well if in the future people are programming Clojure but not Common Lisp, for example, but others might disagree.

“We don’t know what language engineers will be coding in in the year 2100. However, we do know that it will be called FORTRAN.” — C.A.R. Hoare