The evolution of development and coding

From the evolution history of development and coding

The history of computer programming can be traced back to the early twentieth century. The original electronic computers were programmed using machine language (machine code) Programming, this is also known as the first generation programming language. However, working with machine code is difficult because programmers must write programs using an instruction format of 0s and 1s, and it is easy to make mistakes.

In the 1950s, high-level programming languages ​​came into being, which were the second generation of programming languages. The first high-level programming language was FORTRAN (Formula Translation), developed by IBM for writing scientific and engineering applications. Later, other high-level programming languages ​​appeared:

• COBOL (Common Business-Oriented Language), which is a language used for business data processing.
• LISP (List Processor) is a language used for artificial intelligence and machine learning.
• BASIC (Beginner's All-purpose Symbolic Instruction Code) is a language used between education and small computers.

In the 1960s, the third generation of programming languages ​​began to emerge. These programming languages ​​were characterized by structured programming and the use of compilers to convert code into machine code. This makes it easier for programmers to write code, reducing the chance of errors. Some of these popular programming languages ​​include:

• C Language: Developed by Dennis Ritchie at Bell Labs in 1972 for the development of the Unix operating system. Today, C remains one of the most popular programming languages.
• Pascal: Developed by Niklaus Wirth, primarily for educational and scientific applications.
• Ada: Developed by the U.S. Department of Defense for programming high-reliability systems and real-time systems.

In the 1980s and 1990s, the fourth generation of programming languages ​​emerged. These languages ​​are designed for specific domains and tasks, usually associated with databases and other business applications. Some of these programming languages ​​include:

• SQL (Structured Query Language): A language used to operate and manage relational databases.
• MATLAB: A high-level programming language for scientific and engineering computing.
• Python: A popular programming language used in data analysis, scientific computing, web development and other fields. Python is also one of the most commonly used languages ​​in the fields of artificial intelligence and machine learning.

Today, we have a wide variety of programming languages ​​to choose from, each with its own advantages and disadvantages. As technology continues to evolve and new applications emerge, we can expect more programming languages ​​and tools to emerge in the future.

Move from test-driven development to observability-driven development.

Test-driven development (TDD) is a development methodology in which writing test cases is an important step in the development process. By writing test cases, we can ensure the correctness and reliability of the code. However, as systems become more complex, it becomes increasingly difficult to ensure their correctness using traditional testing methods. Therefore, Observability-Driven Development (OOD) becomes a new solution.

OOD is a development approach based on monitoring and collecting application runtime information. Through continuous monitoring and collection of applications, we can better understand their behavior and performance, identify potential problems and respond promptly.

Specifically, OOD emphasizes the following aspects:

1. Monitoring applications: OOD can monitor each application function and all components involved in the system, thereby Understand application performance and behavior.
2. Collect data: OOD can collect data in the application in various ways, such as event logs, tracing, metrics, etc., for subsequent analysis and optimization.
3. Analyze data: OOD can analyze collected data using various tools and techniques, such as machine learning, artificial intelligence, etc., in order to predict and detect potential problems.
4. Optimize applications: By analyzing the collected data, OOD can quickly identify potential problems and make corresponding fixes to optimize application performance and behavior.

Observability-driven development has the following advantages compared to test-driven development:

1. Better deal with complexity: As systems become more and more complex, test-driven development becomes a difficult method to deal with complexity. And OOD can deal with complexity through real-time monitoring and analysis of applications.
2. Better speed and efficiency: In traditional test development, test cases and code can take a lot of time and effort to write and maintain. OOID can improve development speed and efficiency by finding and resolving problems faster.
3. Better scalability: OOD can be easily expanded to larger systems and provide more data and analysis tools. This allows developers to better understand application behavior and performance, allowing them to better optimize their applications.

Are developers looking to expand beyond coding?

Developers may want to expand into areas other than coding, this may be due to the following reasons:

1. Interests: Some developers are interested in other areas such as design, product development, data analysis, etc. Have a strong interest in learning, practicing and applying knowledge and skills in these areas.
2. Development: For developers who want to gain more development opportunities and improve their skills in their careers, learning knowledge and skills in other fields can help them work more efficiently with professionals in other fields. and better understand business needs.
3. Adapt to market changes: Many companies are now turning to full-stack development, data-driven development, DevOps and other fields. Therefore, developers may need to learn some related technologies and knowledge to meet business needs and Changes in the market.

4. Improve efficiency: In some cases, expanding into areas other than coding may increase productivity. For example, knowledge in the design field or product development field can help developers design better. and develop user-friendly applications.

Overall, whether a developer wants to expand beyond coding depends on their personal interests and career goals, as well as the needs of their company and industry.

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