A cognitive approach to evaluating programming languages

Programmers have been known to engage in flame wars about programming languages (and related matters like choice of text editor, operating system or even code indent style). Rational arguments are absent from these heated debates, differences in opinion usually reduce to personal preferences and strongly held allegiances without much objective basis. I have discussed this pattern of thinking before as found in politics.

Although humans have a natural tendency to engage in this type of thought and debate for any subject matter, the phenomenon is exacerbated for fields in which there is no available objective evidence to reach conclusions; no method to settle questions in a technical and precise way. Programming languages are a clear example of this, and so better/worse opinions are more or less free to roam without the constraints of well established knowledge. Quoting from a presentation I link to below

Many claims are made for the efficacy and utility of new approaches to software engineering – structured methodologies, new programming paradigms, new tools, and so on. Evidence to support such claims is thin and such evidence, as there is, is largely anecdotal. Of proper scientific evidence there is remarkably little. – Frank Bott

Fortunately there is a recognized wisdom that can settle some debates: there is no overall better programming language, you merely pick the right tool for the job. This piece of wisdom is valuable for two reasons. First, because it is most probably true. Second, its down to earth characterization of a programming language as just a tool inoculates against religious attitudes towards it; you dont worship tools, you merely use them.

But even though this change of attitude is welcome and definitely more productive than the usual pointless flame wars, it does not automatically imply that there is no such thing as a better or worse programming language for some class of problems, or that better or worse cannot be defined in some technical yet meaningful way. After all, programming languages should be subject to advances like any other engineering tool The question is, what approach can be used to even begin to think about programming, programs, and programming languages in a rigorous way?

One approach is to establish objective metrics on source code that reflect some property of the program that is relevant for the purposes of writing better software. One such metric is the Cyclomatic complexity as a measure of soure code complexity. The motivation for this metric is clear, complex programs are harder to understand, maintain and debug. In this sense, cyclomatic complexity is an objective metric that tries to reflect a property that can be interpreted as better/worse; a practical recommendation could be to write and refactor programs code in a way that minimizes the value of this metric.

But the problem with cyclomatic complexity, or any measure, is whether it in fact reflects some property that is relevant and has meaningful consequences. It is not enough that the metric is precisely defined and objective if it doesn’t mean anything.  In the above, it would be important to determine that cyclomatic complexity is in fact correlated with difficulty in understading, maintaining, and debugging. Absent this verified correlation, one cannot make the jump from an objective metric on code to some interpretation in terms of better/worse, and we’re back where we started.

The important thing to note is that correctly assigning some property of source code a better/worse interpretation is partly a matter of human psychology, a field whose methods and conclusions can be exploited. The fact that some program is hard to understand (or maintain, debug, etc) is a consequence both of some property of the program and some aspect of the way we understand programs. This brings us to the concept of the psychology of programming as a necessary piece in the quest to investigate programming in a rigorous and empirical way.

Michael Hansen discusses these ideas in this talk: Cognitive Architectures: A Way Forward for the Psychology of Programming. His approach is very interesting, it attempts to simulate cognition via the same cognitive architectures that play a role in artificial general intelligence. Data from these simulations can cast light as to how how different programming language features impact cognition, and therefore how these features perform in the real world.

The ACT-R cognitive architecture

I have to say, however, that this approach seems very ambitious to me. First, because modeling cognition is incredibly hard to get right. Otherwise we’d already have machine intelligence. Secondly, because it is hard to isolate the effects of anything beyond a low granularity feature. And programming languages, let alone paradigms, are defined by the interplay of many of these features and characteristics. Both of these problems are recognized by the speaker.


[1] Image taken from http://www.lackuna.com/2013/01/02/4-programming-languages-to-ace-your-job-interviews/

Make static type checking work for you

(Beta reduction)

When first using programming languages like C, C++, Java, I never stopped to think about what the compilation phase was actually doing, beyond considering it a necessary translation from source code to machine code. The need to annotate variables and methods with types seemed an intrinsic part of that process, and I never gave much thought about what it really meant, or the purpose it served; it was just part of writing and compiling code.

Using dynamic languages changes things, you realize that code may need some form of translation or interpretation for execution, but does not absolutely require type annotations as with statically typed languages. In this light, one reconsiders what a type system is from scratch, how it plays a part in compilation, and what compilation errors and type errors really are.

A type error (I am not talking about statistics) is an important concept to grasp well. In fact not just for programming, it is also a useful concept to illuminate errors in natural language and thinking. Briefly, a type error is treating data as belonging to a kind to which it does not in fact belong. The obvious example is invoking operations on an object that does not support them. Or alternatively, according to this page

type error: an attempt to perform an operation on arguments of the wrong types.

If one tries to perform such illegal operations (and there is no available conversion or coercion), the program can behave unexpectedly or crash. Statically typed languages can detect this error at compile time, which is a good thing. This is one of the main points advocates of statically typed languages make when discussing static and dynamic languages.

But the question is, how big an advantage is this? How important are type errors, what fraction of common programming errors do they make up? And how much of program correctness (in the non strict sense of the term) can a compiler check and ensure?

Compilers and type systems can be seen not just requirements to write programs that do not crash, but also tools with which to express and check problem domain information. The more information about the problem domain can be encoded in the type information in a program, the more the compiler can check for you. In this mindset one adds type information order to exploit the type system, rather than just conform to it.

Here are a few examples where problem domain information is encoded in types in order to prevent errors that otherwise could occur at runtime, and therefore would need specific checks. A concrete example taken from the first post I linked

  • If I call move on a tic-tac-toe board, but the game has finished, I should get a compile-time type-error. In other words, calling move on inappropriate game states (i.e. move doesn’t make sense) is disallowed by the types.
  • If I call takeMoveBack on a tic-tac-toe board, but no moves have yet been made, I get a compile-time type-error.
  • If I call whoWonOrDraw on a tic-tac-toe board, but the game hasn’t yet finished, I get a compile-time type-error.

By encoding these rules of the problem domain into the type system, it is not possible to write a program that violates the rule, logic errors in the program do not compile.

But it is unrealistic to go all the way and say, all the relevant information should be expressible in types, and its really seductive twin: all program errors are type errors. Unfortunately, the real world is not that tidy and convenient, unless you’re doing specialized things like theorem proving, or programming with an exotic programming language like Coq.

As advocates of dynamic languages know very well, there is no subsitute for unit testing and integration tests. The compiler should not make you feel safe enough to disregard testing. Compile-time checking and testing are complementary, not mutually exclusive. Compile-time checking does not eliminate the need for testing, nor does testing eliminate the benefits of compile-time checking.

Still, there is most probably room left to express more relevant information in the type system and making the compiler do more work for you. And this becomes even more plausible if you’re using a language with such a powerful type system as Scala. More on these matters: Static Typing Where Possible, Dynamic Typing When Needed: The End of the Cold War Between Programming Languages.

IO, throughput and blocking: a simple model

I’ve recently come across some comments and questions that discuss the idea of non-blocking IO and how it works. Which led me to this video

described as

..here is a short video that tries to explain the advantage of non-blocking javascript. It’s oversimplifying but it should illustrate the basic concept.

A simple model

Continuing with oversimplification, here I’ll present a model that describes throughput for a blocking and non-blocking IO process where messages have to be sent across a communication channel. This channel is characterized by two parameters, bandwidth and latency, that together with a third parameter, message size, completes the model. Thus

m = message size — the size of the messages to be sent

b = bandwith — the channel’s capacity, data that can be sent per unit time

l = latency — round tri,p time for a ping message

and what we want to identify is the throughput, as a function of these parameters

t = throughput — messages processed per unit time = f(m, b, l)

Throughput is defined as the amount of message replies arriving at the source per unit time, which is equivalent to the rate of messages arriving at the target under the assumption that message replies are not constrained by bandwidth. This throughput is what defines the performance of the IO process. We’ll use arbitrary units for the quantities, but you can think of message size, bandwidth and latency in the usual units (ie Kb, Kb/s, ms) if that makes it clearer.

First, the non-blocking throughput is simply the bandwidth divided by the message size

tn = b / m

this results from the assumption that non-blocking message sending will achieve 100% bandwidth utilization; the number of messages per unit time is directly given by the bandwidth.

Blocking

With blocking, each message will only be sent once a reply has been received for the previous one; previous messages block subsequent ones. This causes the number of message replies that arrive at the source per unit time to not be fully constrained by bandwidth, but by bandwidth and latency. The net effect is to add a time overhead to each message’s total processing time. Since this overhead occurs per message, we first calculate transmission time for one message using available bandwidth, which is

transmission time = message size / bandwidth

the overhead is half the latency, since that is the time it takes for a message reply to arrive, triggering the next message send. So the total time per message is

total time = transmission time + return delay = m / b + l / 2

because this is the total time required for each message send, the throughput is the reciprocal

tb = 1 / (m / b + l / 2)

This is the function we were looking for. As a sanity check, we can see that if the latency is zero, the above equation reduces to the non-blocking case. This also shows that t≤ tn for all values.

Note that this throughput corresponds both to that of message replies and messages arriving at the destination, the important point is that the latency blocks further sends, besides adding time to the roundtrip reply. Finally, if delays corresponding to message processing were variable, the term l / 2 would have to be substituted with the average processing time plus half the latency to yield an average throughput.

Plotting f

Let’s plot f using some example values to see what happens. We will use the following

message size = 5

bandwidth = 1-100

latency = 0.01-1.0

To compare performance with and without blocking, we’ll define a quantity

r = ratio of blocking throughput to non-blocking throughput = tb / tn

We will use octave to generate 3D plots for this data, using this simple script

function draw(message)
b = [1:100];
l = [0.01:0.01:1];
[x,y] = meshgrid(b,l);
r = (1./((message./x) + y/2))./(x/message);
n = x/message;
b = (1./((message/x) + y/2));
figure(1);
mesh(x,y,n);
figure(2);
mesh(x,y,b);
figure(3);
mesh(x,y,r);
endfunction

which generates three plots, one for non-blocking throughput, one for blocking throughput, and one for the ratio. The vertical axis shows the throughput, with the two horizontal axes corresponding to bandwidth (left) and latency (right). Message size was fixed at 5 units.

Non-blocking throughput (tn)

Nothing special going on here, throughput scales linearly with bandwidth. Because there is no blocking, latency has no effect, the surface is a plane.

Blocking throughput (tb)

The interesting thing to note here is how latency (bottom right axis) controls the degree to which throughput scales with bandwidth (bottom left axis). With high latency, throughput barely scales, the curve rises but is almost flat. At the other extreme, with zero latency the scaling is the same as in the previous graph, a straight line with the same gradient rising up to the red region. The transition from no scaling to scaling occurs in the middle of the graph, as the shape of the surface changes. Overall, throughput is of course reduced in comparison to non-blocking.

Ratio (r = tb / tn)

A ratio of 1.0, equal throughput, occurs with zero latency, the peak at the top right that continues along the ridge at the top of the graph. But what is interesting is the other ridge that extends towards the bottom. In fact, the trend that we can see from the blue area towards the latency axis is that lower bandwidth produces higher ratios closer to 1.0, equal performance. After a moment’s thought, this makes sense, the lack of bandwidth negates the effects of blocking. Or in other words, blocking still manages to utilize 100% bandwidth when it is scarce.

Javascript and non-blocking IO

The last point I’d like to make here is that the association that some people make between javascript and non-blocking IO is wrong. Javascript as a language says nothing about blocking or non-blocking IO. What javascript as a language does have is anonymous functions (or lambdas, or whatever you want to call them). Anonymous functions as first class citizens makes it easy to define callbacks, and it is through these callbacks that non-blocking IO can occur. But in this day and age, not having anonymous functions is a stone age thing rather than the norm, so most modern languages should be able to do non-blocking IO with ease.