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Language Designers

Once you have a tool to design languages in an agile way, we face the most difficult task: designing them.

The designing of a good language is the main part of the process as it will be the tool of the developers and it will support the semantic intelligence of the system.

Furthermore, since the concepts of the language structure the model itself, before the design it is necessary to know deeply the domain being modeled.

Features of a good language

  • High level of abstraction. The higher the level the more powerful will be the language and higher the semantic meaning of the concepts. Also, a high level of abstraction denotes a deep knowledge of the domain to be modeled.
  • Simple. It should be easy to use and read. A simple language is often synonymous with a high level of abstraction.
  • Different levels of complexity. While it should be simple, it should also allow ways to define deeply details by those who need it.
  • Aesthetically pleasing.
  • Semantically powerful. In order for a language to be productive, it is only required to provide its concepts with a graphical representation and translations to traditional languages, but if we really want it to be powerful, we must give the concepts other semantic interpretations: auto documentation, auto validation, inference rules, etc.
Requirements of a good designer
From the features of a good language we can derive the requirements:

  • Business oriented. Getting a deep knowledge of the domain, in order to design the language, requires a high concern in all the processes that govern it.
  • Abstraction skills. Once the domain is known, analytical skills are required in order to identify, with the highest possible level of abstraction, its purest essence.
  • Focused on semantics. The language has to be designed under the perspective of finding concepts with a high capacity to represent knowledge.
  • Qualities focused on simplicity and aesthetics.
This new development paradigm requires a particular profile to design languages, where not only the former analytical skills are important but also the capability to provide usability and knowledge-representation capacity to the language.

At first, it may seem a complex task but, after all, is part of the evolution of technology where the profiles that contribute most are those with greater capacity for abstraction.

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Domain-Specific Languages (DSLs) are programming languages designed to define, in a more accurate and expressive way, particular domains, whether technical or business domains.

They are named like this as opposed to General Purpose Languages (GPLs – Java, C #, C + +, etc.), providing a narrower but more accurate approach. Their goal is: covering only the domain for which are designed, but with the most suitable grammatical structures and / or graphic abstractions .

Those languages can be analysed under the point of view of two different perspectives: as an evolution from code generation or as an evolution from GPLs.

From Code Generation to DSLs
There are different ways, more or less sophisticated, to generate code: macros, table-structured data, dynamic generation, parsing, CASE tools, etc, but none of them as powerful as a language (textual or graphical), which define, in a formal way, linguistic structures, human readable representations and semantics .

Therefore, we can see DSLs as the most powerful way of Code Generation.

From GPLs to DSLs
GPLs are powerful because they can be used to solve all the problems (Turing Complete) but in many cases are poorly expressive due to the big gap between the problem domain (real world) and the solution domain (source code). In those cases programming and maintenance is difficult because it’s not easy to understand (read and write) what the program tries to solve. For instance, we can compare the definition of a Web UI and its HTML code: the expressive gap is huge.

DSLs bridge those gaps.

Features and benefits of the DSLs

  • Higher level of abstraction. They define more complex concepts, more abstract and therefore more intentional, more expressive.
  • Less degrees of freedom. Normally they are not Turing complete. They allow to define the domain and nothing but the domain with the rules that govern the domain, which makes them very powerful (on that domain, of course).
  • Productivity. Programming with them is efficient and more streamlined.
  • Software quality. They abstract away technical complexity reducing errors. That complexity is usually solved by the generator.
  • IDE Support. Validations, type checking, code completion, etc. This is a huge advance compared with abstractions via APIs or Frameworks.
  • Platform Independent.
  • And all the benefits of code generation.
DSLs are common in real life; throughout history they have been created in maths, science, medicine…Now is time to use them in software development.

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Why Code Generation?

Code generation is not a new style or technique, it’s the path followed by programming languages to deal with complexity, from binary coding to first, second and next generation languages. It’s what compilers have been doing for ages.

The key subject here is “deal with complexity”, the more complex is your problem more abstract has to be your thinking. In other words, you need to raise the level of abstraction. And this rule applies equally to the tool used to solve the problem: the programming language.

Therefore, we can state that “raising the level of abstraction is the goal pursued by the evolution of programming languages”.

The common languages used today to solve the problems( Java, C#, C++, Delphi…), are known as general purpose languages (GPLs), and here is the problem, they are “general purpose”, it means they can solve “all” the problems but from a global perspective. They can solve them from a level of abstraction wide enough to reach the solution but not as high as we need on each particular problem. There is a gap between the level of abstraction we use to deal with the problem and the level of abstraction we use to solve it via GPLs.

How can we bridge that gap? Obviously, with code generation.

As a conclusion, to tackle properly a problem, we need to find a particular language to define the solution in the level of abstraction each particular problem requires. In order to make that solution computable, we need to generate code, usually in the nearest lower level: GPL’s.

Those particular languages are known as DSLs: Domain-Specific Languages, but this subject will be addressed in another post.

As a conclusion, we can note that this approach does not only apply to business problems but also to solving technical problems. For example, the new challenges offered desktop-web applications require a more abstract approach that integrates all technologies: HTML, CSS, JavaScrpit, AJAX and others.

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