The first generation of codes used to program a computer, was called machine language or machine code, it is the only language a computer really understands, a sequence of 0s and 1s that the computer’s controls interprets as instructions, electrically.
The second generation of code was called assembly language, assembly language turns the sequences of 0s and 1s into human words like ‘add’. Assembly language is always translated back into machine code by programs called assemblers.
High Level Language
The third generation of code, was called high level language or HLL, which has human sounding words and syntax (like words in a sentence). In order for the computer to understand any HLL, a compiler translates the high level language into either assembly language or machine code. All software programming languages need to be eventually translated into machine code for a computer to use the instructions they contain.
As the end user you do not see the code used to create computer software programs. However, you do use the results and the end products of today’s software programming are soft programs that are easy to use by the consumer. Below you can find several software programs listed, each article discusses the history of software programming and the lives of the software programmers behind your favorite software programs.
Reverse engineering is the process of taking a software program apart and analyzing it with the intention to construct a new program that does the same thing without actually copying anything from the original and avoiding copyright or patent infringement.
Computer programming (often shortened to programming or coding) is the process of designing, writing, testing, debugging, and maintaining the source code of computer programs. This source code is written in one or more programming languages. The purpose of programming is to create a set of instructions that computers use to perform specific operations or to exhibit desired behaviors. The process of writing source code often requires expertise in many different subjects, including knowledge of the application domain, specialized algorithms and formal logic.
Whatever the approach to software development may be, the final program must satisfy some fundamental properties. The following properties are among the most relevant:
- Reliability: how often the results of a program are correct. This depends on conceptual correctness of algorithms, and minimization of programming mistakes, such as mistakes in resource management (e.g., buffer overflows and race conditions) and logic errors (such as division by zero or off-by-one errors).
- Robustness: how well a program anticipates problems not due to programmer error. This includes situations such as incorrect, inappropriate or corrupt data, unavailability of needed resources such as memory, operating system services and network connections, and user error.
- Usability: the ergonomics of a program: the ease with which a person can use the program for its intended purpose, or in some cases even unanticipated purposes. Such issues can make or break its success even regardless of other issues. This involves a wide range of textual, graphical and sometimes hardware elements that improve the clarity, intuitiveness, cohesiveness and completeness of a program’s user interface.
- Portability: the range of computer hardware and operating system platforms on which the source code of a program can be compiled/interpreted and run. This depends on differences in the programming facilities provided by the different platforms, including hardware and operating system resources, expected behavior of the hardware and operating system, and availability of platform specific compilers (and sometimes libraries) for the language of the source code.
- Maintainability: the ease with which a program can be modified by its present or future developers in order to make improvements or customizations, fix bugs and security holes, or adapt it to new environments. Good practices during initial development make the difference in this regard. This quality may not be directly apparent to the end user but it can significantly affect the fate of a program over the long term.
- Efficiency/performance: the amount of system resources a program consumes (processor time, memory space, slow devices such as disks, network bandwidth and to some extent even user interaction): the less, the better. This also includes correct disposal of some resources, such as cleaning up temporary files and lack of memory leaks.
Readability of source code
In computer programming, readability refers to the ease with which a human reader can comprehend the purpose, control flow, and operation of source code. It affects the aspects of quality above, including portability, usability and most importantly maintainability.
Readability is important because programmers spend the majority of their time reading, trying to understand and modifying existing source code, rather than writing new source code. Unreadable code often leads to bugs, inefficiencies, and duplicated code. A study found that a few simple readability transformations made code shorter and drastically reduced the time to understand it.
Following a consistent programming style often helps readability. However, readability is more than just programming style. Many factors, having little or nothing to do with the ability of the computer to efficiently compile and execute the code, contribute to readability. Some of these factors include:
- Different indentation styles (whitespace)
- Naming conventions for objects (such as variables, classes, procedures, etc.)
The academic field and the engineering practice of computer programming are both largely concerned with discovering and implementing the most efficient algorithms for a given class of problem. For this purpose, algorithms are classified into orders using so-called Big O notation, which expresses resource use, such as execution time or memory consumption, in terms of the size of an input. Expert programmers are familiar with a variety of well-established algorithms and their respective complexities and use this knowledge to choose algorithms that are best suited to the circumstances.