Know Well More Than Two Programming Languages
Editor’s Note: Learning multiple languages is key to anyone’s success as a programmer. Dr. Russel Winder gained experience with learning and teaching multiple languages first as Professor of Computing Science and head of the department of computer science at King’s College London, then later working at various startups. Now retired, he continues to share his passion for learning programming languages, and here shares how learning multiple languages and paradigms can change your approach to programming for the better.
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The Psychology of Programming
People have known for a long time now that programming expertise is related directly to the number of different programming paradigms that a programmer is comfortable with — that is, not that he just knows about or knows a bit of, but that he can genuinely program with.
Every programmer starts with one programming language. That language has a dominating effect on the way that programmer thinks about software. No matter how many years of experience the programmer gets using that language, if she stays with that language, she will know only that language. A one-language programmer is constrained in her thinking by that language.
A programmer who learns a second language will be challenged, especially if that language has a different computational model than the first. C, Pascal, Fortran — all have the same fundamental computational model. Switching from Fortran to C introduces a few, but not many, challenges. Moving from C or Fortran to C++ or Ada introduces fundamental challenges in the way programs behave. Moving from C++ to Haskell is a significant change and hence a significant challenge. Moving from C to Prolog is a very definite challenge.
We can enumerate a number of paradigms of computation: procedural, object-oriented, functional, logic, dataflow, etc. Moving among these paradigms creates the greatest challenges.
Why are these challenges good? That has to do with the way we think about the implementation of algorithms and the idioms and patterns of implementation that apply. In particular, cross-fertilization is at the core of expertise. Idioms for problem solutions that apply in one language may not be possible in another language. Trying to port the idioms from one language to another teaches us about both languages and about the problem being solved.
Cross-fertilization in the use of programming languages has huge effects. Perhaps the most obvious is the increased and increasing use of declarative modes of expression in systems implemented in imperative languages. Anyone versed in functional programming can easily apply a declarative approach even when using a language such as C. Using declarative approaches generally leads to shorter and more comprehensible programs. C++, for instance, certainly takes this on board with its wholehearted support for generic programming, which almost necessitates a declarative mode of expression.
The consequence of all this is that it behooves every programmer to be well skilled in programming in at least two different paradigms, and ideally at least the aforementioned five. Programmers should always be interested in learning new languages, preferably from an unfamiliar paradigm. Even if their day job always uses the same programming language, the increased sophistication of use of that language when a person can cross-fertilize from other paradigms should not be underestimated. Employers should take this into account and allow room in their training budget for employees to learn languages that are not currently being used, as a way of increasing the sophistication of the languages that are being used.
Although it’s a start, a one-week training course is not sufficient to learn a new language: it generally takes a good few months of use, even if part-time, to gain a proper working knowledge of a language. It is the idioms of use, not just the syntax and computational model, that are the important factors.
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Dr. Russel Winder was first a theoretical high energy particle physicist and then retrained himself as a Unix systems programmer. This led to him becoming a computer science academic (University College London, then King’s College London) interested in programming; programming languages, tools, and environments; concurrency; parallelism; build; human–computer interaction; and sociotechnical systems. Having been Professor of Computing Science and head of the department of computer science at King’s College London, he left academia to dabble with start-ups as CTO or CEO. After this he was an independent consultant, analyst, author, trainer, and expert witness for a decade before retiring in 2016. He is still very interested in programming; programming languages, tools, and environments; concurrency; parallelism; and build — it keeps him active during retirement.
