The advent of high-level programming languages like fortran and cobol in the 1950s revolutionized software development. These languages abstracted many of the complexities associated with hardware, allowing programmers to focus on solving problems rather than managing hardware constraints. This shift not only made programming more accessible but also significantly boosted productivity. Today, we have a plethora of high-level languages, each designed with specific use cases in mind, from web development with javascript and python to system programming with c and rust.

The impact of modern programming languages on software development

Modern programming languages continue to shape the landscape of software development. Languages like python, javascript, and go are known for their simplicity and efficiency, making them popular choices for developers. Python, with its readable syntax and extensive libraries, is widely used in data science, machine learning, and web development. Javascript remains the backbone of web development, enabling dynamic and interactive user experiences.

The rise of functional programming languages such as haskell and scala has also influenced mainstream languages, encouraging the adoption of functional programming paradigms. This paradigm shift promotes immutability and pure functions, leading to more predictable and maintainable code. Additionally, languages like rust are gaining traction due to their focus on memory safety and concurrency, addressing common issues faced by developers working on large-scale and performance-critical applications.

The integration of modern programming languages with various development tools and environments has further streamlined the development process. Continuous integration/continuous deployment (ci/cd) pipelines, advanced debugging tools, and integrated development environments (ides) enhance developer productivity and code quality. As programming languages evolve, they continue to integrate new features that cater to the ever-changing needs of developers and the industry.

The role of SEO in modern web development

In the realm of web development, the importance of search engine optimization (seo) cannot be overstated. Seo is crucial for ensuring that websites are discoverable and rank well on search engines like google. This visibility is vital for driving organic traffic and ensuring the success of online businesses. Programming languages like html, css, and javascript play a significant role in seo by shaping the structure, style, and functionality of web pages.

When building a website, developers must consider seo best practices to optimize content, improve load times, and ensure a mobile-friendly design. For instance, clean and semantic html code helps search engines better understand the content of a page. Proper use of meta tags, alt attributes for images, and header tags are essential elements of on-page seo. Additionally, javascript frameworks such as react and angular offer seo-friendly features that enhance the crawlability and performance of web pages.

For those looking to enhance their website’s seo, it’s beneficial to consult with experts in the field. Dr. Seo, for example, provides a comprehensive suite of services to optimize websites and improve their search engine rankings. By leveraging their expertise, businesses can ensure their websites are well-optimized and achieve better visibility in search results.

Future Trends in Programming Languages

As technology continues to evolve, so do programming languages. The future promises exciting advancements that will further transform software development. One of the emerging trends is the increased adoption of machine learning and artificial intelligence in programming languages. Languages like Python and R are already popular in the data science community due to their powerful libraries and frameworks. Future languages and updates to existing ones will likely focus on making machine learning more accessible and efficient.

Another significant trend is the push towards greater concurrency and parallelism. With the proliferation of multi-core processors, programming languages need to efficiently manage concurrent tasks. Languages like Go and Rust are leading the charge with built-in features for handling concurrency. As applications become more complex and data-intensive, the ability to perform parallel processing will be crucial for performance and scalability.

Moreover, there is a growing emphasis on security in programming languages. As cyber threats become more sophisticated, developers need tools that help them write secure code. Rust, for example, is designed with safety in mind, preventing many common programming errors that can lead to security vulnerabilities. Future languages will likely continue this trend, incorporating more robust security features to help developers protect their applications from attacks.

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 For more detailed insights, check out this article on the pull request meaning. Effectively managing pull requests can lead to faster feature releases and more satisfied developers. Conversely, poor management of pull requests can have the opposite effect.

Now, let’s explore several effective pull request strategies that can be implemented within your team to benefit authors, code reviewers, and the overall workflow.

Before discussing these best practices, it is crucial to comprehend why traditional pull request methods frequently fall short of expectations.

Common Pitfalls in Traditional Pull Request Practices

Traditional pull request workflows commonly encounter a few significant issues:

Overloading Pull Requests

Pull requests may become cumbersome when they contain extensive modifications across different segments, complicating both the review process and comprehension. For example, a single pull request could encapsulate everything from changes to an algorithm that makes recommendations, a complete overhaul of the homepage, and a transition to using a microservices architecture. This could affect hundreds of files. Such extensive changes can be difficult for reviewers to effectively test and approve each element, potentially slowing down the integration process and obstructing further progress.

Hidden Dependencies

Pull requests might inadvertently establish dependencies that intertwine with future features or unrelated functions, leading to a complex and convoluted code structure. Consider a pull request that aims to optimize database queries but ends up diminishing the performance of a product search feature due to changes in the indexes. Such modifications can trigger broad performance issues and these dependencies might emerge unexpectedly, increasing the complexity within the codebase.

Narrow Review Focus

Effective code reviews should cover a range of aspects beyond mere code quality and functionality, including performance, user experience, and the impact on the overall architecture. Issues often arise when reviewers concentrate solely on immediate coding problems, overlooking these wider considerations. This narrow focus can result from a lack of specialized knowledge in certain areas, leading to reviews that are both incomplete and insufficient.

Review Process Slowdowns

Bulky pull requests can lead to review exhaustion, which in turn causes delays and dips in team efficiency. Relying on a limited number of reviewers with specific skills can result in bottlenecks, slowing the entire review process and hindering the distribution of knowledge among team members. 

For example, progress may be delayed on a complex pull request related to an algorithm if it must wait for the only team member qualified to review it, thereby halting subsequent developments.

Comprehensive Review Strategy

It is crucial for code reviews to fully assess the ramifications of changes being proposed. For instance, while reviewers might endorse the implementation of caching to boost performance, they may miss the resultant rise in infrastructure expenses and complexity, which could adversely affect the system.

Lack of Context

Often, pull request descriptions and commit messages do not provide enough context, which complicates the review process. A typical scenario might involve a developer submitting a pull request with a vague title like “Fix bug” that changes a crucial module without offering detailed explanations. This lack of detail forces reviewers to either seek more information from the developer or meticulously analyze the codebase to understand the changes, resulting in inefficiencies and possible oversights.

These issues highlight the need for a more thoughtful and thorough method in handling pull requests to streamline the process and improve results.

Optimizing Development Speed with 8 Pull Request Best Practices

Efficiency in code reviews is vital, but promoting collaboration and knowledge sharing within teams is just as important. Engineering leaders can adopt several key practices to streamline PR workflows and minimize friction.

Opt for Smaller Pull Requests

One highly effective practice for pull requests is to maintain their size small and well-defined, targeting fewer than 200 lines of changes per pull request, with an optimal size being under 50 lines of code.

Statistically, it’s observed that teams managing an average of 50 lines per pull request deliver 40% more code than teams handling pull requests of more than 200 lines. Not only do smaller pull requests simplify writing comprehensive unit tests for each component, but they also facilitate easier rollback of any errors that occur.

When confronted with a large code review that seems daunting due to its size, a common and effective strategy is to request the developer to break down the change list into several smaller, sequential change lists. This approach assists in managing review workloads more effectively.

By keeping pull requests short, it encourages reviewers to evaluate code changes more swiftly and prevents them from feeling overwhelmed. Additionally, it aids in crafting commit messages that concisely summarize the changes, which is particularly useful for capturing the necessary details without excess complexity.

However, adapting to a model of smaller pull requests might pose a challenge for teams accustomed to a different workflow in handling pull requests. In such cases, methods like pull request stacking could be beneficial.

Using PR Stacking in Practice

Stacked PRs break down large changes into a series of small, iterative modifications that build on one another. The initial pull requests set up the basic infrastructure, with each subsequent one adding enhancements or features. This method allows for separating larger features into manageable parts and enables parallel progression of different workstreams without delays.

The concept of stacked PRs involves making incremental changes directly on the main branch. A developer starts with a minor modification, commits it as a small pull request, and continues this process, creating several small changes in sequence. This approach accelerates the review process and allows for quicker integration into the main branch.

For instance, a developer working on a recommendation algorithm might divide the development into three segments: the core algorithm, the database interactions, and the user interface components. Such division not only speeds up reviews but also helps in merging changes into the main branch more efficiently.

While GitHub supports PR stacking, there are specialized tools designed to manage dependencies across feature branches. These tools facilitate automatic rebasing and synchronizing the cascading changes among connected, stacked PRs, freeing teams from the intricate git operations so they can focus on more significant challenges.

Offer Detailed Context

It’s also crucial for PR authors to include comprehensive contextual details in their submissions. It should be standard practice for descriptions to articulate the reasons for changes clearly, including links to related domain logic and previous discussions about alternatives or limitations.

For changes that affect the user interface, it is helpful to provide before-and-after screenshots to visibly demonstrate the UI and functional differences. Authors should detail the edge cases checked through unit tests in various settings, including different browsers and datasets.

Moreover, authors need to explicitly mention any breaking changes, potential impacts on downstream integration, or compatibility issues. Highlighting these elements ensures that changes that carry higher risks are thoroughly examined during the review process.

Integrate Structural Protections

In software development, implementing architectural safeguards is essential to prevent a gradual decline in quality due to numerous incremental changes. Prioritize the development of modular components, as these facilitate easier and faster testing and code releases.

Using abstraction effectively is crucial. By breaking down functions into modular parts, you can significantly simplify the complexity of the system, enhance its extensibility, and improve maintainability. Additionally, consider updates carefully to ensure compatibility with previous versions, only opting to phase out legacy systems when there are well-coordinated plans in place.

It is also beneficial to assign review responsibilities to those who oversee the impacted components. Such individuals are more likely to understand the potential downstream effects and maintain a comprehensive perspective on the changes.

Promote Instant Collaboration

Encouraging developers to seek feedback and collaborate in real-time while working on features and fixes can be more effective than relying solely on pull request reviews. Explore practices like rapid prototyping, pair programming, informal reviews, and live discussions to foster a continuous flow of ideas and facilitate immediate collaboration.

The goal is to foster a supportive environment where asking questions is seen as an integral part of responsible development. By publicly recognizing and rewarding such behaviors, you reinforce them. Additionally, strive to streamline the feedback process, making it intuitive and effortless for developers.

Ultimately, the aim is to create a supportive, appreciated, and productive work environment for developers, minimizing any obstacles that could hinder their workflow.

Engage Various Expertise

The selection of the right reviewers for each pull request is crucial and often overlooked. Instead of assigning reviewers randomly, carefully choose both technical and non-technical experts who are best suited to validate the modifications.

For changes to core infrastructure, involve senior engineers who specialize in platform reliability to evaluate the operational impacts. For updates related to the user interface, include UX designers who can advocate for the end-user’s needs.

Security reviews should be mandatory for changes that involve encryption, access controls, and defining exposure boundaries. With such specialized review processes, the evaluations are not only more efficient but also more accurate, as reviewers can concentrate on their areas of expertise without the need to shift focus frequently. Small pull requests further improve this process by allowing for precise concerns to be addressed, enabling the assignment of reviewers based on their specific domain knowledge.

Manage Team Workloads Proactively

Effectively scaling code review involves balancing the team’s bandwidth with the volume of incoming pull requests to prevent overwhelming backlogs.

Start by setting clear expectations for pull request turnaround times that reflect the team’s capacity, and try to limit the number of work-in-progress pull requests. Tracking metrics such as “time to first review” and “time from publish to merge” can highlight areas for improvement in your pull request process.

Encourage timely reviews by promoting a standard where, if not engaged in a critical task, code reviews should be conducted shortly after their receipt. Ideally, a code review should be responded to within one business day.

When considering adjustments to this process, it’s important to collect feedback from engineers and other stakeholders to gauge their views and ensure any process changes meet the team’s needs and preferences.

Integrate Automation Across Workflow

When submitting a pull request (PR), it is beneficial to include an automated code review and linting stage. Implement tools such as GitHub Actions, Jenkins, SonarQube, or Checkstyle to catch syntax errors, uphold style guidelines, and pinpoint security risks.

Adopting this strategy will:

• Allow human reviewers to devote more time to evaluating the business logic;
• Maintain consistent style and quality throughout the codebase;
• Identify nuanced or complex issues that might be overlooked by manual reviews.

Integrating automated reviews as a mandatory step in the PR process helps eliminate fundamental flaws like minor style discrepancies, code complexity, and security vulnerabilities early on. This automation addresses common, superficial problems, enabling human reviewers to focus on more intricate aspects of logic and user experience.

Moreover, seek ways to automate additional stages of the development lifecycle, such as code analysis, testing, and release management. The main objective is to enhance developer efficiency, not merely to apply strict rules mechanically.

Efficiently managed pull requests can alleviate workflow bottlenecks and boost team dynamics over time. This effectiveness stems from structuring changes in a manner that clarifies them for reviewers and encourages expert feedback from various disciplines.

Review processes should transform into constructive dialogues that empower developers to progress swiftly and accumulate relevant knowledge. Sustained improvements in speed result from continuously refining these systems.

Key strategies include:

• Monitoring important metrics to shape experiments with new workflows;
• Automating routine tasks to save mental energy for critical activities;
• Initiating discussions within the team to share best practices and solicit feedback on gradual changes in workflow.

The real advantage manifests when a culture of ongoing enhancement is established. Pull requests then become collaborative touchpoints that propel the team towards optimal performance and excellence, provided there is a real commitment to implementing and consistently improving these processes.

Achieving Ongoing Optimization of Pull Requests

For sustained effective collaboration, it is essential for teams to constantly refine their working environments by leveraging metrics, sharing knowledge, and implementing smart automation strategies.

• Implement Metrics Monitoring: Keep track of crucial metrics like the speed of review turnarounds, the number of bugs identified, and the duration until merges are completed. Hold regular retrospective meetings to evaluate these metrics, identify trends, and pinpoint areas for improvement;
• Conduct Controlled Experiments: Test different approaches to review frequencies, methods of PR communication, and the integration of various tools to determine the most effective strategies. Regularly collect feedback from developers to gauge what enhancements are effective and what aspects may be causing issues. Introduce new processes on a small scale initially, allowing the team time to adjust before broader application;
• Promote Knowledge Exchange: Set aside time for developers to engage in informal code reviews with one another, fostering skill development and early feedback prior to formal reviews. Organize workshops to train team members on the best practices for crafting and evaluating pull requests. Make it a point to publicly acknowledge and celebrate instances of proactive collaboration within the team.

Conclusion 

Pull requests provide an opportunity to build connections, encourage growth, and combine team talents into collective achievements greater than individual contributions. Teams that actively optimize pull requests create a positive feedback loop, driving both product and team improvement.

However, realizing the potential of new PR workflows requires intention and care. The key factor is whether teams can invest the necessary time and effort into streamlining the technical and cultural infrastructure needed for seamless collaboration.

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Discover the Basics of Python Programming

Python is an incredibly powerful programming language that is being used in many industries today. It is an open source language, meaning anyone can contribute to the development of Python. Python is an object-oriented language, which means it is designed to help you create programs that are easier to maintain and reuse.

Python is a great choice for people just starting out with programming. It has a very easy-to-understand syntax, which makes it easier for beginners to learn. It also has a lot of libraries and packages that can be used to create complex programs quickly and easily. Additionally, the large number of online tutorials and resources available make it easier to get started with Python.

The first step to learning Python is understanding the basic principles of programming. This includes variables, data types, functions, and control flow. Variables are used to store data, such as numbers or strings. Data types are used to store different types of data, such as integers, floats, or booleans. Functions are blocks of code that can be called upon to perform a task. Control flow allows you to create programs that make decisions based on certain conditions.

Once you understand the basics of programming, you can start exploring more advanced topics in Python. This includes object-oriented programming, which allows you to create objects and classes for better code organization and readability. Additionally, you can learn about modules and packages, which are collections of related code that can be imported into other programs.

Python also has a variety of libraries and frameworks that can be used to create web applications and other software. Popular frameworks include Django, Flask, and Pyramid. Popular libraries include NumPy and SciPy for scientific computing and TensorFlow for machine learning.

Finally, Python has a vibrant community of developers who are always willing to help out newbies with their questions. There are many online forums where you can ask questions and get help from experienced programmers. Additionally, there are many conferences and meetups where you can meet other Python developers and learn more about the language.

Overall, Python is an incredibly powerful language that is easy to learn and use. With the right resources and guidance, you can quickly start creating amazing programs with Python. So why not give it a try?

Learn How to Create Programs Using Python

Learning how to create programs using Python is an important skill to have. Python is a powerful language that can be used to create powerful applications. It is a great language for beginners as it is easy to learn and use.

Python is an interpreted language, which means that the source code written in Python is converted into machine code that can be executed by the computer. This makes it easy to write and debug programs in Python. Python is also an object-oriented language, which means that it allows you to create objects, classes, and functions that can be used in a program.

To get started with learning how to create programs using Python, you will need to install the Python interpreter on your computer. Once the interpreter is installed, you will be able to write and run Python programs on your computer. There are many online tutorials and books available for learning how to create programs using Python.

When you are ready to start creating programs, you will need to decide which version of Python you want to use. Python 3 is the latest version of Python and it includes many new features. If you are just starting out with programming, it is recommended that you use Python 3.

Once you have chosen the version of Python that you want to use, you will need to install an editor. An editor allows you to write and edit your source code. There are many editors available for free online, such as Notepad++, Sublime Text, Atom, and Visual Studio Code. Once you have installed an editor, you can begin writing your program in Python.

The syntax of Python is relatively simple and easy to learn. It has a lot of built-in functions and libraries that allow you to perform complex tasks easily. The syntax of Python also follows the principle of readability, which means that your code should be easy to understand by anyone who reads it.

In order to create more complex programs, you will need to learn how to use libraries and frameworks in Python. Libraries are collections of code written in Python that can be used in your programs. Frameworks are collections of libraries that allow you to quickly create applications with minimal effort. Learning how to use libraries and frameworks will allow you to create powerful applications quickly and easily.

Once you have learned the basics of programming in Python, you can begin exploring more advanced topics such as object-oriented programming, web development, game development, data science, machine learning, and more. With the help of online tutorials and books, you can learn how to create powerful applications using Python quickly and easily.

Develop Your Skills With Python Exercises

Python is a powerful, versatile programming language that can be used to develop a wide variety of applications. It is also a great language to learn and practice your programming skills. Python exercises are a great way to get your feet wet in the language and hone your skills.

Python exercises come in many forms and can range from simple tasks to complex problems. They can involve coding problems, logic puzzles, and even games. No matter what type of exercise you choose, they can all help you learn and practice your Python skills.

When it comes to coding problems, Python exercises can range from the simple to the complex. Simple problems can involve basic data types and operations, while more complex problems may require more advanced concepts such as classes and objects. The great thing about coding problems is that they can be solved with different approaches and levels of difficulty. This allows you to challenge yourself and learn new things about the language.

Logic puzzles are another type of Python exercise that can help you hone your problem-solving skills. Puzzles such as Sudoku, Knapsack, and 8-Queens can help you learn how to think logically and come up with solutions to challenging problems. These puzzles also require you to think outside the box and come up with creative solutions.

Finally, Python exercises can also take the form of games. These games can range from simple text-based adventures to more complex 3D simulations. Games can help you learn how to use the language in different ways and can also be a fun way to practice your skills.

Overall, Python exercises are a great way to learn and practice your programming skills. Whether you’re just getting started or are looking for ways to improve your existing skills, there are plenty of exercises available for every level of experience. So take some time to explore the different types of Python exercises and find one that’s right for you!

Take Your Python Knowledge to the Next Level

Python is one of the most popular programming languages in the world, and it’s an essential skill for aspiring software engineers and developers. But as a language, it can be challenging to learn and master. It’s important to take your Python knowledge to the next level in order to stay competitive in the job market.

One way to do this is to take advantage of online resources. There are a variety of tutorials, guides, and videos available online that can help you learn more about Python and become more proficient in its use. You can also join online forums and chat rooms where experienced Python developers can provide you with advice and guidance.

Another way to take your Python knowledge to the next level is to attend a boot camp or workshop. These intensive programs are designed to help you quickly learn the fundamentals of Python and apply them to real-world problems. Many boot camps also offer additional programming classes that can help you develop more advanced skills.

Finally, you can also consider taking a certification exam. These exams will test your knowledge of Python and provide you with a certification that can be used to demonstrate your proficiency in the language. This can be a great way to show potential employers that you have the skills they’re looking for.

No matter which path you choose, taking your Python knowledge to the next level is essential for staying competitive in today’s job market. With the right resources and dedication, you can become an expert in Python and open up many new opportunities for yourself.

Get Started on Your Python Journey Today!

Python is one of the most popular and powerful programming languages in the world. It’s used to create everything from websites and apps to artificial intelligence and robotics. Whether you’re a beginner or an experienced programmer, you can get started on your Python journey today.

Python is an easy language to learn and use. It’s well-documented, open-source, and has a vibrant online community of developers who are always willing to help newcomers. It also has powerful libraries that make it easy to work with data, create visualizations, and more. All of this makes it a great choice for anyone looking to get started with coding.

The best way to get started with Python is to start writing some code. There are plenty of free tutorials and resources available online, so you don’t need to buy any books or software to get started. You can also find tons of tutorials on YouTube that will take you through the basics step by step.

Once you’ve learned the basics, it’s time to start building something. You could create a simple text-based game, a website, or even a mobile app. There are lots of tutorials available online that can help you get started. If you’re feeling brave, you could even try creating something from scratch!

To really take your Python skills to the next level, it’s worth investing in some additional resources. There are plenty of books and online courses available that will give you a deeper understanding of the language and help you become a better programmer.

Finally, don’t forget to join some Python communities online. This is a great way to stay up-to-date with the latest developments in the language and get help from other developers when you need it. You can also find job postings, hackathons, and other events where you can network with other Python developers.

Starting your Python journey doesn’t have to be intimidating or overwhelming. With the right resources and support network, you can become a proficient Python programmer in no time. So why wait? Get started on your Python journey today!

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A novice programmer often has a lot of homework and lots of assignments to complete at college or university. Luckily, there are online resources like call-tracking.org that allow you to hire experts to help you with your programming homework and more.

1. Class Concept

Class is the first core idea in object-oriented programming. A class is an abstract structure that provides two perspectives on real-world objects: its attributes (properties) and methods (the actions it can perform or its behavior).

Employees, for instance, can be represented as a class in object-oriented programming. In this case, the class Employees represents all of the employees, and its properties include surname, first name, address, and date of birth. Operations on employees include changing their salaries, going on leave, retiring, etc.

All class instances are referred to as objects and are created from the Class through the instantiation process. The Class is ultimately a mold or template. Every object is a class instance as a result.

Three unique methods are used during the instantiation of a class, and understanding these methods is crucial.

Constructor

There are three different kinds of builders:

The copy constructor (or copy constructor) takes a single argument of the same type as the object to be created (typically in the form of a constant reference), copies the attributes from the object passed in the argument to the object to be created, and is called by default when an object is created (offered by default during compilation if there is no declared constructor).

If the constructor’s signature matches, the parametric constructor is invoked.

(Get) Accessors and Mutators (set)

These special methods let you access a class’s properties and change them outside, much like an API. They enable the functionalities of the Class to be “called” from the outside.

Without explicitly gaining access to the properties of a class instance, accessors let you retrieve their values from the outside. By preventing its alteration, they secure the property in this way. The mutators provide you the ability to change the value of the properties while verifying that the value you wish to assign to the feature complies with the semantic restrictions placed on the Class.

Destructor

The class instance’s life is ended by this method. It may be called either explicitly or implicitly when the object is erased.

2. Object Concept

The object is the second-most significant concept in object-oriented programming. An object is an instance of a class, as we previously stated. The item resembles a house constructed according to a certain plan in several ways. As long as designers follow this blueprint, all of their homes will be the same.

Technically, an object has three characteristics:

·         a name, address, or reference that clearly identifies the object as having that identity;

·         specifies that each object has a default value for each of its properties (when specified at instantiation). The states of the object are these values;

·         Each object has a set of methods that it can use to carry out any declared actions or behaviours. In OOP, these activities are concretely expressed as methods. Calls to these methods or messages sent by other objects start the potential activities on an object.

3. The Encapsulation Concept

Encapsulation is the third principle of object-oriented programming.

Only the properties of objects are accessible through its methods. Since the methods and attributes that enable altering the objects regardless of their states are contained within the Class, this is possible.

The encapsulation inhibits object alteration outside of its methods and limits direct access to the states. For instance, if you have a Car class and want to set the color property’s value to blue, you must use a method like “define the color” that was put in place by the Class developer. The varied color values can be limited using this technique.

Encapsulation is a system that stops access to or change of items using methods other than those that are suggested. The items’ integrity is ensured by it.

4. The Inheritance Concept

The fourth fundamental idea in object-oriented programming is inheritance. It is an OOP concept that describes how a class can take on traits (such as attributes and methods) from another class.

Classes have parent classes from which their objects can inherit properties. For instance, we may create a Manager class, which is a specialized class of Employee and inherits its properties.

In OOP, inheritance has two key benefits:

·         Reuse: You don’t have to create the same Class each time for each specialized Class;

·         Specialization: A new class reuses the characteristics and methods of a class by adding operations particular to the new Class.

5. Polymorphism concept

Polymorphism is the final fundamental idea in object-oriented programming. If an object-oriented language can recognize an object as an instance of several different classes at once, it is polymorphic. For instance, Java is a polymorphic language.

Conclusion

Since object-oriented programming is the basis of many high-level programming languages, developers should have a firm grasp of it. By applying the core OOP concepts to understand how simple programs work, you can find the root causes of bottlenecks and remove them by developing more inventive code. Coding suites, learning new languages, and a grasp of OOP ideas can all help you improve your skills.

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Unlike the first versions of PHP, in which the object-oriented approach has long remained an alternative to the traditional procedural approach, new versions of PHP are almost entirely designed for object-oriented development.

Traditionally, when getting acquainted with any programming language, it is necessary to get acquainted with its advantages and disadvantages.

Advantages of PHP

Web development. To a greater extent, the PHP programming language was created and maintained as a language focused on website development. The language constructs are designed for the convenience of working in the Web environment.

Multiplatform. PHP works on almost all popular operating systems, so there will be no problems when developing and transferring projects.

Free of charge. PHP programming language is completely free. In addition, you do not need to pay for most additional editors, servers and databases with which it works. A significant number of software products will have source code available for study and modification.

Ease of learning. It will not be difficult to learn PHP and start creating ready-made programs, compared to its competitors, such as .NET, Python, Ruby, etc.

Disadvantages of PHP

Lack of a leader. Commercial technologies have, so to speak, their leader, an architect who determines the look and direction of technology development. PHP has no leader, many solutions and designs are a compromise of interest groups and historically formed realities.

Feedback from the past. The PHP programming language is not young, and it has passed a very difficult path of development, which can be characterized by the creation and deletion of keywords, directives, constructs that are quite ambiguous. For example, some constructions seem to exist, but it is not recommended to use them. The same applies to directives that at first glance were supposed to facilitate development, but in practice turned into serious security problems.

Disconnected community. PHP is one of the first technologies for developing web projects, with the help of which more than half of the Internet was created. The development of PHP simultaneously involved a huge number of programmers around the world, which in turn gave rise to a huge number of different approaches, frameworks and incompatible ecosystems. Over time, alternative implementations of the RNG have emerged, resulting in incompatible ecosystems within the RNG, and the community needs to spend considerable effort to create the same libraries within different groups. To some extent, the situation is being addressed by RNR standards, which describe common rules and interfaces to ensure framework interoperability, but this process is still in its early stages, while competing technologies (.NET, Ruby) already have a common platform for all frameworks.

PHP structure

PHP programming language consists of a core and extensions. It is quite difficult to draw a line between the core and extensions, since many extensions are included in the core or distributed as easily installed compiled binary libraries.

PHP code created by a programmer can be divided into the following types:

Components – are PHP libraries that are built using the Composer package manager;

Frameworks – thousands of ready-made assemblies, often from components, with which you can create websites of any complexity.

Ready-made applications – ready-to-use PHP developments. These are CMS content management systems, database management web interfaces (phpMyAdmin).

Related technologies

PHP allows you to quickly develop websites, but additional software and technologies will be required to run it:

Web server. A program that provides interaction between the client and your application using the HTTP protocol. For the most part, Nginx or Apache web server will be enough to operate the website. Installing a web server will not create difficulties, because on the Internet you can easily find its installation on all known platforms. Installing Apache 2.4 web server with PHP 8 module and MariaDB database.

Database server. To store data, database servers are used, of which there are a very large number today. Each individual database deserves a separate book.

Git version control system, which serves to store development history, backup, upload code to the server, organize teamwork. Whether you work alone or in a team – Git has now become the main tool of a modern programmer, no matter what programming language you use.

This list can be continued for a very long time, but it is safe to say that by learning the PHP programming language you will be satisfied with your choice.

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In the 1990s, C++ became one of the most widely used general-purpose programming languages.

Features
When creating C++, they tried to maintain compatibility with C. Most C programs will work properly with the C++ compiler. C++ has a syntax based on the C syntax.

The innovations of C++ in comparison with C are

support for object-oriented programming through classes;
support for generic programming through templates;
additions to the standard library;
additional data types;
exception handling;
namespaces;
built-in functions;
operator overloading;
function name overloading;
references and operators for managing freely allocated memory.

In 1998 the international standard of C++ language was ratified: ISO/IEC 14882 “Standard for the C++ Programming Language”. The current version of this standard is ISO/IEC 14882:2003.

Example of the program “Hello, world!”
Below is an example of a simple C++ program that displays the string Hello World on the standard output channel.

include

int main()
{
std::cout << “Hello, world!” << std::endl;
return 0;
}

History of the name
The name “C++” was invented by Rick Mascitti and was first used in December 1983. Earlier, at the development stage, the new language was called “C with classes”. The resulting name comes from the C operator “++” (increasing the value of a variable by one) and the common way of assigning new names to computer programs, which is to add a “+” symbol to the name to indicate improvements. According to Straustrup, “this name indicates the evolutionary nature of C changes”. The expression “C+” was the name of an earlier programming language, not related to C++.

Some C programmers may note that if the expressions x=3; y=x++; are executed, the result will be x=4 and y=3, because x is incremented only after assigning it to y. However, if the second expression is y=++x; then you will get x=4 and y=4. Based on this, we can conclude that it would be more logical to call the language not C++, but ++C. However, both c++ and ++c expressions increase c, and besides, the c++ expression is more common.

Pedants may also note that the introduction of C++ does not change C itself, so the most accurate name would be “C+1”.

Technical overview
In 1998, the C++ language was standardized by the International Organization for Standardization under the number 14882:1998 – C++ Programming Language. Currently, an ISO working group is working on a new version of the standard, codenamed C++09 (formerly known as C++0X), which should be released in 2009.

The 1998 C++ standard consists of two main parts: the language core and the standard library. The C++ standard library incorporated the STL template library, which was developed simultaneously with the standard. Now the name STL is not officially used, but in the circles of C++ programmers this name is used to denote the part of the standard library that contains the definitions of container templates, iterators, algorithms and functionors.

The C++ standard contains a normative reference to the C standard from 1990 and does not independently define those functions of the standard library that are borrowed from the standard C library.

In addition, there are a huge number of C++ libraries that are not included in the standard. Many C++ libraries can be used in C++ programs.

Standardization has defined the C++ programming language, but this name can also hide incomplete, limited pre-standard variants of the language. Initially, the language developed outside the formal framework, spontaneously, as the tasks set before it. The development of the language was accompanied by the development of the Cfront cross-compiler. Innovations in the language were reflected in the change of the cross-compiler version number. These version numbers of the cross-compiler also applied to the language itself, but for the present time we are not talking about the versions of the C++ language.

Standard library
The C++ standard library includes the standard C library with small changes that make it more appropriate for the C++ language. Another large part of the C++ library is based on the Standard Template Library (STL). It provides important tools such as containers (e.g. vectors and lists) and iterators (generic pointers) that provide access to these containers as arrays. In addition, STL allows you to work in a similar way with other types of containers, such as associative lists, stacks, queues.

Using templates, you can write generalized algorithms that can work with any containers or sequences whose members are accessed by iterators.

As well as in C, the capabilities of libraries are activated by using the #include directive to include standard files. In total, 50 such files are defined in the C++ standard.

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Python is an easy-to-use, yet full-featured programming language that provides many more tools for structuring and maintaining large programs than shell. On the other hand, it handles errors better than C, and, being a very high-level language, has built-in high-level data types such as flexible arrays and dictionaries, which are time-consuming to implement efficiently in C.

Due to more general data types, Python is applied to a wider range of tasks than Awk and even Perl, at the same time many things in Python are done just as easily.

Python allows you to break programs into modules that can then be used in other programs. Python comes with a large library of standard modules that can be used as the basis for new programs or as examples when learning the language. Standard modules provide tools for working with files, system calls, network connections, and even interfaces to various graphics libraries.

Python is an interpreted language, which saves a significant amount of time usually spent on compilation. The interpreter can be used interactively, which allows you to experiment with the capabilities of the language, write program templates or test functions during bottom-up development. It is also handy as a desktop calculator. Python allows you to write very compact and easy-to-read programs. Programs written in Python are usually much shorter than the equivalent in C or C++ for several reasons:

high-level data types allow you to express complex operations with a single instruction;
grouping of instructions is performed by indentation instead of curly braces;
there is no need to declare variables;

Python is an extensible language: knowledge of C allows you to add new embedded functions or modules to perform critical operations with maximum speed or write an interface to commercial libraries available only in binary form. The Python language interpreter can be embedded in a program written in C and used as an extension or command language for that program. Python is currently used by tens of thousands of programmers worldwide, and the number of people using it is rapidly growing, doubling and tripling every year. Python attracts users for a number of reasons. It is used for program development and allows for much faster development than traditional languages like C, C++ or Java.

This language works equally well on Windows, UNIX, Macintosh, and OS/2, and can be used to easily develop small applications or scripts as well as to deploy large programs. Python offers access to a powerful and easy-to-use set of 29 graphical user interface tools. Traditional machine languages like C and Pascal share a number of characteristics, such as strict typing, basic types, complex (and usually long) loops, and the need for large amounts of code to perform relatively small tasks. Java is fairly new, but shares most of the characteristics included in this list. Programmers familiar with traditional languages will agree that the lack of strict typing makes Python easier to work with.

Python in comparison with other languages
There are many differences between Python and other languages, let’s list the main ones:

Memory management is fully automatic – no need to worry about allocating or freeing memory. There is no threat of an “unsafe reference”. Java is the only language offering such a concept.
Types are associated with objects, not variables. This means that a variable can be assigned a value of any type, and that (for example) an array can contain objects of different types. Traditional languages do not provide this feature.
Operations are usually performed at a higher level of abstraction. This is partly a result of the way the language is written and partly a result of the extensive standard code library that comes with Python.

These and other features of Python make deploying applications extremely fast. The performance of the created application depends on its features. Of course, for a numerical algorithm that performs simple integer arithmetic in a ‘for’ loop, it does not matter what language it is written in. But for an “average” application, the performance increase can be amazing. One drawback of Python, compared to most traditional languages, is that it is not a fully compiled language; instead, it partially translates the program to an internal form of bytecode, and this bytecode is executed by the Python interpreter.

However, in the long run – modern computers have so much unused computing potential that for 90% of applications, performance is related to the choice of language. Java also compiles to bytecode, but currently runs slower than Python in most cases. In addition, it is very easy to combine Python with modules written in C or C++, which can be used to increase the speed of programs in critical areas.

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