Programming Languages

 Programming Languages

 

 Scratch is an excellent free-of-charge resource for beginning programmers like me. Scratch utilizes block code, each block consisting of simple commands that have embedded code.  My experience with Scratch at first was somewhat daunting-that is, before I viewed the many available tutorials.  I enjoyed using a sprite of my choice and using block code to create movement, sounds, looks, controls, and events.  I chose a dog named Fido as my sprite and had him perform various movements, sometimes dictated by the user of the program, and other times based strictly on the instructions in the code.  Here is my block of code:  https://scratch.mit.edu/projects/1175993550/editor.  Even this simple program produced on Scratch demonstrated the need to identify both the program’s objective and how to accomplish said objective.  Only then can one develop code to realize the goal of the program.  The need for such protocol will only increase as the software becomes more and more arduous.

Programming in Scratch is much easier than utilizing compiled, interpreted, binary, assembly, SQL, and runtime code.  Apart from Scratch, compiled and interpreted languages would be the next easiest to program in.  These languages use common human language that in the end is eventually translated into binary code, a code the microprocessor can run directly.  The difficulty in programming in these languages is learning the grammar and syntax each one uses.  Query language would be the next in order of simplicity.  This language allows for the creation of tables that can produce queries that can then be turned into reports.  A query language may seem simple because of the ease with which it can be utilized; however, there is a lot going on behind the scenes that the user never sees.  The most difficult and cumbersome of the languages, behind only binary, is assembly language.  In this low-level language, substantially more code must be written for a given purpose.

The choice of which computer language to use, either compiled or interpreted, depends on the platform you’re writing for, the operating system of the device, the necessary performance of the application, and individual or institutional preferences.  An instance in which an interpreted language would be most appropriate as compared to a compiled language is where speed is not critical, when software must be run on multiple operating systems, and where access to all the computer hardware is not of major importance.  An instance where interpreted code would be more appropriate is in the creation of programs for utilization on the web.  This web-based software needs to be capable of running on Linux, Windows, and iOS.  Interestingly enough, another area where interpreted language has an advantage over compiled languages is in the development of machine learning applications.  According to Wiejak and Smolka, teaching a computer to perform a specific task using a comparable type of algorithm is more efficient when programming in interpreted languages.  This increased efficiency was realized in the run time of the particular application, the number of lines of code needed, and the accuracy of the computer in performing a given task (Weijak & Smolka, 2024).

In comparison to interpreted languages, compiled languages run more quickly because a compiler translates the source code into a binary-executable file before the application is run.  This binary, in turn, can be run on a system without the need for additional processing.  Another advantage of compiled languages is these have access to all the computer’s hardware.  The major disadvantage, however, of coding in compiled languages is the language is platform dependent.  An example of where compiled coding would be most appropriate is in graphics-intensive programming, and in particular, gaming software (Kwame et al., 2017).

 

References

Kwame, A. E., Martey, E. M., & Chris, A. G. (2017). Qualitative assessment of compiled, interpreted and hybrid programming languages. Communications on Applied Electronics, 7(7), 8-13.

Wiejak, T., & Smołka, J. (2024). Performance of machine learning tools. Comparative analysis of libraries in interpreted and compiled programming languages. Journal of Computer Sciences Institute, 33, 339–345. https://doi.org/10.35784/jcsi.6589