The Shape of Code

I thought it would be useful to list the books that gripped me one way or another this year (and may be last year since I don’t usually track such things closely); perhaps they will give you some ideas to add to your Christmas present wish list (please make your own suggestions in the Comments). Most of the books were published a few years ago, I maintain piles of books ordered by when I plan to read them and books migrate between piles until eventually read. Looking at the list I don’t seem to have read many good books this year, perhaps I am spending too much time reading blogs.

These books contain plenty of facts backed up by numbers and an analytic approach and are ordered by physical size.

The New Science of Strong Materials by J. E. Gordon. Ideal for train journeys since it is a small book that can be read in small chunks and is not too taxing. Offers lots of insight into those properties of various materials that are needed to build things (’new’ here means postwar).

Europe at War 1939-1945 by Norman Davies. A fascinating analysis of the war from a numbers perspective. It is hard to escape the conclusion that in the grand scheme of things us plucky Brits made a rather small contribution, although subsequent Hollywood output has suggested otherwise. Also a contender for a train book.

Japanese English language and culture contact by James Stanlaw. If you are into Japanese culture you will love this, otherwise avoid.

Evolutionary Dynamics by Martin A. Nowak. For the more mathematical folk and plenty of thought power needed. Some very powerful general results from simple processes.

Analytic Combinatorics by Philippe Flajolet and Robert Sedgewick. Probably the toughest mathematical book I have kept at (yet to get close to the end) in a few years. If number sequences fascinate you then give it a try (a pdf is available).

Probability and Computing by Michael Mitzenmacher and Eli Upfal. For the more mathematical folk and plenty of thought power needed. Don’t let the density of Theorems put you off, the approach is broad brush. Plenty of interesting results with applications to solving problems using algorithms containing a randomizing component.

Network Algorithmics by George Varghese. A real hackers book. Not so much a book about algorithms used to solve networking problems but a book about making engineering trade-offs and using every ounce of computing functionality to solve problems having severe resource and real-time constraints.

Virtual Machines by James E. Smith and Ravi Nair. Everything you every wanted to know about virtual machines and more.

Biological Psychology by James W. Kalat. This might be a coffee table book for scientists. Great illustrations, concise explanations, the nuts and bolts of how our bodies runs at the protein/DNA level.

Will the number of commonly used programming languages converge to a few that remain commonly used for ever, will there be many relatively common languages in use, or will the (relatively) commonly used languages change over time?

There are plenty of advantages to having one programming language that everybody uses for ever. English+local dialects seems to be heading towards becoming the World’s one native language, but the programming language world seems to be moving in the direction of diversification and perhaps even experiencing changing popularity of those in common use.

What are the forces that drive programing language usage?

Existing code. If a company wants to maintain and update its software products it needs to hire people to use the language they are written in. This is a force that maintains the status quo.

Existing programmer skills. When given the task of writing new software where language usage is not specified, developers are likely to pick a language they already know. In the case of group development the choice is made by group leaders. This is a force that maintains the status quo.

Fashion. Every field has fashions and programming language usage is no exception. Using a particular language can be seen as sexy, leading-edge, innovative, the next big-thing, etc. Given the opportunity some developers will chose to learn and write code in this language.

Desire to learn a new language. Some developers like to learn new things and this includes programming languages. Given the opportunity such developers will sometimes chose to learn and write code in a language they find interesting.

The cost of creating and implementing a new language continues to be within the reach of one individual who is willing to invest the considerable effort required. Hundreds, if not thousands of new languages have been created every year almost since computers were first invented. The only change here over the last 40 years is probably an increase in the number of new languages.

What has changed in the last 15 years is ease of transmission (e.g., a ubiquitous computing platform and the Internet) and the growth of the fashion industry (e.g., book publishers).

Computing is bathed in newness. New products, new chips, new gadgets, new software, new features, new and improved, the latest. What self respecting developer would want to be caught dead using a language invented before they were born?

Publishers need a continuous stream of new subjects that will drive customers to buy books. What better subject than a hot new programming language?

At the moment we seem to be living through a period of programming language usage divergence. Will this evolutionary trend continue or are we currently in the Cambrian explosion period of software engineering evolution?

What are the forces acting against the use of new languages? At the moment the only significant forces acting against the use of new languages are existing source and existing developer expertise. There are weaker forces, for instance, the worry that in the years to come it will be difficult to find developers to maintain existing software written in what has become an obscure language, but most software has a short lifetime and in many application domains this is not an issue. Whether the fashion for newness will eventually diminish enough to significantly slow the take-up of new languages remains to be seen.