Know Well More than Two Programming Languages

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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 they stay with that language, they will only know that language. A ''one language'' programmer is constrained in their thinking by that language.
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 they stay with that language, they will only know that language. A ''one language'' programmer is constrained in their thinking by that language.
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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 form C to Prolog is a very definite challenge.
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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 between these paradigms creates the greatest challenges.
We can enumerate a number of paradigms of computation: procedural, object-oriented, functional, logic, dataflow, etc. Moving between these paradigms creates the greatest challenges.
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Why are these challenges good? It is 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.
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Why are these challenges good? It is 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.
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.
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The consequence of all this is that it behoves every programmer to be well skilled in programming in at least two different paradigms, and ideally at least the five mentioned above. Programmers should always be interested in learning new languages, preferably from an unfamiliar paradigm. Even if the 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 on board and allow in their training budget for employees to learn languages that are not currently being used as a way of increasing the sophistication of use of the languages that are used.
The consequence of all this is that it behoves every programmer to be well skilled in programming in at least two different paradigms, and ideally at least the five mentioned above. Programmers should always be interested in learning new languages, preferably from an unfamiliar paradigm. Even if the 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 on board and allow in their training budget for employees to learn languages that are not currently being used as a way of increasing the sophistication of use of the languages that are used.
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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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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.
By [[Russel Winder]]
By [[Russel Winder]]

Revision as of 14:11, 17 August 2009

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 just know about, or know a bit, but genuinely can 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 they stay with that language, they will only know that language. A one language programmer is constrained in their 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 between these paradigms creates the greatest challenges.

Why are these challenges good? It is 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 behoves every programmer to be well skilled in programming in at least two different paradigms, and ideally at least the five mentioned above. Programmers should always be interested in learning new languages, preferably from an unfamiliar paradigm. Even if the 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 on board and allow in their training budget for employees to learn languages that are not currently being used as a way of increasing the sophistication of use of the languages that are 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.

By Russel Winder

This work is licensed under a Creative Commons Attribution 3


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