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Moore's Law

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Next: The Sources of Software Complexity

The implications of Moore's law on software are too often overlooked. As long as hardware complexity grows at an exponential rate, the complexity of the software that runs on this hardware tends to follow the same exponential growth. This is problematic, because the brain of the developers do not follow the same exponential growth pattern. The traditional way to fix this problem has been to invent new so-called software paradigms regularly, yielding repeated discontinuous jumps in productivity, at the expense of most of the existing code base.

Concept Programming in general, and XL in particular, are designed to make the creation of new paradigms a standard part of the software development process. As a result, programmers will be able to benefit Moore's law (by adding new tools to their toolchest as soon as Moore's law makes them practical) rather than merely fighting the ever growing complexity it introduces.

Limits of Software Growth

Figure 1 - FEAST study results

There are a number of ways to illustrate how software developed using a single technology doesn't and cannot follow Moore's law.

The FEAST study focused on the evolution over large periods of time (15 to 20 years) of a variety of unnamed software projects. Some results are shown in Figure 1. Very early on, the projects may follow a somewhat exponential growth (see in particular the yellow curve). But this early enthusiasm quickly leaves place to a much slower pace. The FEAST study actually proposes models of software evolution over time that are well below linear. One might expect a linear growth if programmers could keep adding code at a constant pace. But the FEAST study demonstrates how negative interaction between software components (sometimes known as bugs) slow down this process well below any linear growth.

As an aside, the FEAST study also consider a number of factors impacting the growth, discussing in details the impact of the choice of programming language to long-term evolution of the project. An interesting observation is that the projects written in C tend to degrade over time faster than those written in Cobol! I don't know if this should really be a surprise...

User demands

We made the hypothesis that users demands on software are growing as fast a Moore's law. One might argue that development tapers off simply because there are no more features to add. However, empirical evidence doesn't support that view... Consider the fact that today's software rarely works well on three-years-old computers. The need for speed and features is particularly visible in areas such as games, which always push the envelope of what is possible with a given computer. But even the simplest office suites accumulate features that were simply unthinkable ten years ago, like on-the-fly spelling and grammar corrections.

Figure 2 - Moore's law applied to size of Linux kernel

Another study support the notion that programmers, not users, are the limiting factor. Figure 2 is taken from a published study of the growth of the Linux kernel over time. We can first notice the same pattern of early quasi-exponential growth, followed by a much reduced pace.

But what is particularly interesting in this picture is the difference between the so-called stable and unstable kernels. The stable kernels, after a while, follows the kind of sub-linear growth predicted and observed by the FEAST study. The unstable kernel, on the other hand, keeps growing at a much faster pace. In other words, users would like to add more features, but these features can't be added without disrupting the kernel development process, and so they don't make it to the stable kernel base. This comment is not meant at all as a criticism of kernel developers, but rather as an observation that, even if the abilities of these developers are truly out of the ordinary, they don't improve over time as rapidly as the users would demand it.

The important point is that users, not programmers, are the motivation behind radical changes in software technology. Users wanted Visicalc, graphical user interfaces or the web. Only once the demand was obvious did programmers figure out a way to write such software comfortably and efficiently.

Software paradigms

Figure 3 - Software paradigms

Historically, the problem of growing software complexity has been addressed by creating new software paradigms, which are basically brand new ways to consider software. Figure 3 shows some of the most recent paradigms.

A new paradigm emerges under intense pressure from software users to create a kind of software that the old development model simply didn't consider. For instance, Java's success was fueled by a new kind of web-centric applications or applets, which can be written in C or C++ only at great pains (who wants to use Corba?) Similarly, GUI development was doable in C, but painful and slow (who wants to write X11 callbacks?)

Old languages don't really die, they fade into irrelevance. Thanks to the exponential nature of Moore's law, the new problem space is generally much larger than all other previous software applications combined. So the relevance of any old programming model fades over time. Cobol still exists, but it is simply irrelevant to the vast majority of web-centric software development happening today. C and C++ still exist (and they will remain alive for a long time), but they will never expand to reach the new areas that Java is currently exploring. An interesting twist in that case is that C++ is also exploring a number of areas that Java cannot touch, for instance template meta-programming. And finally, you wouldn't be able to develop anything interesting by today's standards using a 1980's version of Basic or Pascal, not matter how much energy you put into it.

The XL value proposition

There is only one little problem with each new software paradigm. It comes at the expense of only a few million lines of code... XL tries to address that by being a language designed for long-term evolution. Whereas the integration of a new programming model is difficult in older languages, it is considered a key feature of XL.

This kind of evolution is not totally unheard of in the history of programming languages. Lisp in particular offers the basic features necessary for long-term language evolution. It is not an accident if Lisp was one of the first existing languages to integrate the object-oriented paradigm and to make it an ANSI standard. Despite being the second oldest high-level programming language (after Fortran), Lisp remains surprisingly young and strong.

So why XL? Because despite it's surprising agility, Lisp is not a concept-programming language. It causes a higher-than-average level of syntactic noise or semantic noise. An example of syntactic noise is the over-use of parentheses. An example of semantic noise is the existence of a heavy runtime, which is necessary if only to be able to evaluate a Lisp list. XL tries to keep the main benefits of Lisp, without the costs.

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Next: The Sources of Software Complexity

Copyright 2008 Christophe de Dinechin (Blog)
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